94 jobs found

Job Description Job Description Job Title: Palo Alto Firewall Engineer Location: Springfield, VA Type: Contract To Hire Compensation: $80/hr. W2 Benefits available Conversion Salary: $150,000 annually plus benefits Work Model: Onsite Security Clearance: Active TS/SCI clearance and the ability to successfully pass and maintain a U.S. Government polygraph Responsibilities Lead the design, requirements analysis, testing, integration, and implementation of secure network architectures centered on the Palo Alto Networks ecosystem. Serve as the lead technical authority for administering, configuring, and troubleshooting Palo Alto Networks Next-Generation Firewalls (NGFWs) across a hybrid enterprise environment. Engineer and implement solutions for customer Change Requests (CRQs); assess impacts on enterprise transport and security activities and provide technically sound recommendations. Serve as the technical representative for assigned projects and coordinate issues with the appropriate owners, organizations, contract leadership, and customer leadership. Manage the full lifecycle of Palo Alto hardware and software, including complex hardware refreshes, PAN-OS upgrades, legacy-platform sustainment, physical troubleshooting, and line-card replacements. Develop, oversee, and maintain configuration-management processes, network architecture diagrams, technical documentation, integration and test plans, and Standard Operating Procedures (SOPs) for Palo Alto security platforms. Use Panorama for centralized policy management, including templates, device groups, inheritance, and consistent configuration across a diverse fleet of physical and virtual firewalls. Configure and maintain advanced security capabilities and profiles, including App-ID, User-ID, Content-ID, SSL Decryption, WildFire, NAT, IPSec VPNs, and threat prevention. Oversee security-incident reporting, documentation, investigation, and corrective-action development. Act as a liaison to contract/customer management and the government Designated Approving Authority (DAA) regarding network-security status, policies, procedures, and risks. Evaluate and report on new and emerging network-security and communications technologies to improve network capacity, performance, reliability, standardization, and security. Provide mentorship and technical oversight to junior engineers and serve as an escalation point for complex troubleshooting. Follow all customer network-security processes and procedures, maintain compliance with Government and QA standards, and ensure service-performance indicators are met or exceeded. Requirements Security Clearance: Active TS/SCI clearance and the ability to successfully pass and maintain a U.S. Government polygraph. A minimum of 7+ years of hands-on experience administering, configuring, and troubleshooting Palo Alto Networks NGFWs in large-scale enterprise/global environments. Active Palo Alto Networks Certified Network Security Engineer (PCNSE) certification. DoD 8140.01 and DoD 8570.01-M IAT Level II compliance (for example, Security+ CE). Ability to obtain and maintain a CSSP Infrastructure Support certification within 120 days of the start date. Deep practical knowledge of legacy Gen 2/Gen 3 Palo Alto hardware, including PA-3000 and PA-5000 series platforms, legacy CLI, hardware troubleshooting, and line-card replacements. Experience deploying and managing modern PAN-OS architectures, including Prisma Access (SASE), Prisma SD-WAN, and VM-Series virtual firewalls in cloud environments such as AWS, Azure, GCP, or private-cloud. Proven expertise with Panorama for centralized policy management, template/device-group inheritance, and configuration deployment across a hybrid firewall fleet. Advanced understanding of BGP, OSPF, IPSec VPNs, NAT, TLS/SSL, mutual TLS (mTLS), HTTP, SAML, OAuth, OCSP revocation, DoD PKI, Kerberos, LDAP, and Active Directory. Experience with F5 technologies, including APM, AFM, and SSL Orchestrator (SSLO), and troubleshooting TLS/SSL handshake/connection issues. Strong network design, engineering, implementation, and troubleshooting skills, including ROM equipment lists and cost estimates. Knowledge of DISA and Intelligence Community security standards and requirements. Excellent interpersonal skills and technical judgment with the ability to work independently and support weekend/evening work if needed. Bachelor's degree in IT, Cybersecurity, Computer Science, or a related field, with additional relevant experience considered in lieu of a degree. Desired Qualifications Active Palo Alto Networks Certified Network Security Consultant (PCNSC) or Prisma Certified SASE Professional (PCSAE). F5 Networks Certified Technology Specialist (CTS). Cisco CCNP, CCVP, CCNA, CCDP, or equivalent. ITIL v3 Foundations and/or ISC2 CISSP Certification. Proficiency in Python and automation tools such as Ansible, Terraform, or Palo Alto XML/REST APIs. Hands-on experience with Cortex XDR or Cortex XSOAR. Experience with Palo Alto Networks Expedition for migration and rule consolidation. Knowledge of Zero Trust Network Access (ZTNA) architectures and additional security platforms (e.g., Juniper SRX, Cisco FTD/ASA). Master's degree in related fields is a plus. System One, and its subsidiaries including Joulé and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan. System One is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, age, national origin, disability, family care or medical leave status, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law. Ref: Baltimore

Job Description Job Description About Etched Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference . Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history. Job Summary Etched's infrastructure spans some of the most sensitive compute environments in the industry: bare-metal HPC clusters running proprietary ASIC workloads, hybrid on-prem/cloud deployments, and internal toolchains that house irreplaceable chip design IP. As we scale from early silicon to production, securing these environments is foundational - not an afterthought. As our first dedicated Network Security Engineer, you will own the design and implementation of Etched's network security posture end to end. You'll work alongside the infrastructure team to harden our physical and virtual networks, enforce least-privilege access to chip design environments, and build the detection and response capabilities that keep our most sensitive assets safe. This is a high-ownership role for someone who wants to shape security architecture at a company building the compute infrastructure for the next decade of AI - not maintain someone else's stack. Key Responsibilities Design and implement a zero-trust network architecture across on-prem datacenters, multiple office locations, and multi-cloud platforms, including secure remote access that eliminates VPN sprawl without sacrificing engineer usability and speed Define and enforce network segmentation policies that isolate sensitive ASIC development workflows from general infrastructure, customer access, validation labs, and manufacturing infrastructure Balancing prevention and detection, deploy, tune, and operate NDR, IDS/IPS, and next-generation firewalls across our physical and virtual network fabric; build automation to continuously assess and enforce firewall rules, ACLs, and routing policies - treating network security configuration as code Integrate and operate EDR/XDR, MDM/MAM, SASE, and CASB tooling in partnership with end-user and IT teams, enforcing unified DLP policies and device compliance posture across endpoint, cloud, and network control planes to eliminate data exfiltration risk Own our vulnerability management process for network-layer exposure: scanning, prioritization, and remediation tracking in partnership with infrastructure engineers Lead incident response for network-layer security events: detection, containment, root-cause analysis, and post-incident hardening Partner with legal, compliance, and leadership to support regulatory requirements and customer security reviews as they arise Architect and deploy network segmentation for our HPC clusters, isolating EDA tool traffic, ASIC simulation workloads, and CI pipelines from each other and from the corporate network Architect and deploy a ZTNA-based corporate network that eliminates VPN sprawl and ensures end-user devices maintain a consistent security posture and seamless access to sensitive development environments - whether engineers are on-site, remote, or traveling - replacing location-dependent trust with continuous identity and device health verification Design and implement a scalable NDR pipeline that ingests flow data across bare-metal switches and cloud VPCs, feeds a centralized SIEM, and generates actionable alerts with low false-positive rates Develop runbooks and automated playbooks for the highest-probability incident scenarios - credential compromise, lateral movement, and exfiltration from IP-sensitive environments Integrate EDR/XDR telemetry with SASE enforcement and CASB inline controls to build a unified DLP detection and response pipeline spanning endpoints, cloud SaaS, and the corporate network Partner with end-user and IT teams to roll out MDM/MAM policies that containerize sensitive IP on engineer devices and enforce compliance-based conditional access across managed and unmanaged environments You may be a good fit if you have (Must-have qualifications) Bring deep, broad networking expertise - from low-level packet analysis and firewall log forensics to BGP configuration, multi-cloud networking, and CASB/SASE integration across a diverse SaaS landscape Have hands-on experience with the Fortinet ecosystem - firewalls, FortiSASE, FortiAPs, and switches - and are comfortable with Arista switch platforms, including configuration, EOS automation, and integration into a broader security architecture Treat security as an engineering discipline: you write code and automation rather than relying on point-and-click tooling, version-control your configurations, and develop intent-driven network automation Have experience securing high-value compute environments - datacenters, HPC clusters, semiconductor design environments, or similar settings where the cost of a breach is extremely high Have deployed and integrated EDR/XDR, MDM/MAM, SASE, and CASB tooling, and understand how to stitch them together into a unified DLP and access control framework that spans endpoints, cloud, and the network Have built or operated ZTNA-based access models and understand how to enforce consistent security posture across on-site, remote, and traveling users without degrading the experience for engineers Are comfortable owning your domain with minimal oversight: you can independently scope a project, identify the right tooling, and drive it to completion Have strong Linux fundamentals and understand how OS-level networking (iptables/nftables, network namespaces, eBPF) interacts with physical and virtual network security controls Have built or operated network security monitoring at scale - you know the difference between a good alert and noise, and you can architect a detection pipeline that surfaces real signal Can communicate risk clearly to both technical peers and non-technical leadership, and can translate security requirements into actionable infrastructure changes Strong candidates may also have experience with (Nice-to-have qualifications) Experience with EDA environments or semiconductor IP security Familiarity with cloud-native network security controls on AWS, GCP, or Azure (security groups, VPC flow logs, cloud firewalls, CSPM) Background in or exposure to NIST, SOC 2, or ISO 27001 frameworks Experience with eBPF-based network observability and security tooling Benefits Medical, dental, and vision packages with generous premium coverage $500 per month credit for waiving medical benefits Housing subsidy of $2,500 per month for those living within walking distance of the office Relocation support for those moving to San Jose (Santana Row) Various wellness benefits covering fitness, mental health, and more Daily lunch and dinner in our office Unlimited compute budget subject to ROI justification How we're different Etched believes in the Bitter Lesson. We are the first inference-focused frontier AI system. Our addressable market is the entirety of inference, unlike many of our competitors. We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed. Compensation Range: $175K - $275K

Job Description Job Description DevSecOps Engineer (TS/SCI Clearance or Eligibility Required) Location: San Antonio, TX Clearance: Active Top Secret / SCI (or ability to obtain and maintain) Employment Type: Full-Time - On-site Position Overview Seeking a DevSecOps Engineer to design, develop, and maintain on-demand ephemeral test automation ranges in secure environments. This position will support U.S. Air Force and related defense missions by enabling automated test and development pipelines across a variety of virtualization and cloud platforms. The role requires adaptability, rapid prototyping, and strong DevSecOps practices to deliver innovative solutions within highly regulated environments. This is not a traditional enterprise cloud application engineering role. Instead, the engineer will focus on building test automation environments, ephemeral compute stacks, reusable CI/CD components, and custom workflows to accelerate mission software development and validation. Responsibilities Design, implement, and manage GitOps CI/CD workflows leveraging GitLabCI and reusable pipeline components. Develop Infrastructure as Code (IaC) solutions using Terraform, Ansible, and Packer (or similar image-building tools) for both Linux and Windows platforms. Build and maintain ephemeral test environments on AWS, Azure, and local Linux virtualization stacks (Libvirt, QEMU). Develop and maintain Python, Bash, and PowerShell scripts to support test automation, prototyping, and system integration. Rapidly prototype MVP solutions to meet evolving mission requirements, with the ability to refine disposable code into reusable, production-ready modules. Deploy and configure temporary or experimental compute stacks in non-production test environments, including some networking setup/configuration. Support file analysis and novel file type handling for test automation, forensic evaluation, and data fixture management. Collaborate closely with system/software developers to gather requirements and deliver tailored automation solutions. Apply AGILE development practices and ensure strict adherence to SecOps and code hygiene standards. Safely operate across networks of varying classifications, following mandatory data transfer processes and security guidelines. Required Qualifications Active TS/SCI clearance, or the ability to obtain and maintain one. 5+ years of experience in DevOps, Systems Engineering, or Infrastructure Automation. Hands-on experience with: GitOps & CI/CD tools (GitLabCI, Jenkins) IaC tools: Terraform, Ansible Image building: Packer or equivalent for Linux and Windows automation Virtualization platforms: Libvirt, QEMU, AWS, Azure Scripting: Python, Bash, PowerShell Strong ability to prototype solutions quickly and evolve them into modular, reusable systems. Experience with ephemeral environments, automated range deployments, or test infrastructure. Excellent communication skills for gathering requirements and interfacing with developer teams. Familiarity with Agile/Scrum methodologies and secure coding practices. Preferred Qualifications Familiarity with Kubernetes and container orchestration (beneficial but not required). Knowledge of file forensics, data transformation, and test data management in automation contexts. Experience working in defense or classified environments with adherence to RMF/ATO and related compliance standards. Knowledge of emerging technologies including Large Language Model, Machine Learning and Research and Development to support rapid development and prototyping.

Job Description Job Description Job Description ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED We are seeking a versatile, ever-curious Systems Administrator / Systems Engineer (flexible across SA1 to SA3 levels) to join our team in Annapolis Junction, MD. In this role, you will manage, automate, and secure a hybrid, mixed Linux/Windows environment spanning on-premise infrastructure, Proxmox virtualization, and AWS cloud platforms. As an engineer at Nyla, you are a self-starter who thrives on keeping systems running seamlessly while eliminating repetitive tasks through automation. You will leverage modern orchestration frameworks like Ansible and SaltStack, administer Proxmox virtualized infrastructure and CIFS storage shares, and manage cross-platform identity integration using Centrify. You will also play a critical role in enforcing System Security Plan (SSP) compliance and supporting STE/STN standards across managed systems. If you enjoy solving complex systems puzzles in a collaborative, tech-forward environment, Team Nyla is looking for you. The annual base salary range for this role is $115,000-$214,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. , Required Skills Linux System Administration: Deep, hands-on experience administering, configuring, and tuning Linux operating environments within mixed OS ecosystems. Proxmox Virtualization: Practical experience deploying, configuring, and managing Proxmox VE hypervisors and virtual machines, alongside containerization platforms. Infrastructure Automation: Demonstrated experience utilizing Ansible and/or SaltStack for automated deployment, configuration management, and patch execution. Scripting Proficiency: Ability to write clean, maintainable automation scripts using Bash, Python, Perl, or similar scripting languages. Cross-Platform Identity Storage: Experience managing Centrify for Active Directory integration and configuring/managing CIFS network storage shares. Cloud Infrastructure (AWS): Applied operational experience working with AWS cloud environments and services. Security Compliance: Direct experience maintaining SSP compliance, implementing STIGs, and supporting STE/STN operational standards. Education: Bachelor's Degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline, PLUS 5 + years of professional software development experience OR High School Diploma / GED, PLUS 10 + years of hands-on technical software engineering experience in lieu of a degree. , Desired Skills Administrative experience with Windows Server environments, Active Directory Domain Controllers (DCs), and Microsoft Exchange. Knowledge of advanced Kubernetes/EKS container orchestration platforms. Experience integrating CI/CD automation pipelines and Git-based version control for infrastructure management. , About Nyla Technology Solutions Nyla Technology Solutions delivers exceptional Artificial Intelligence (AI), Data Science, and Software Engineering services for the U.S. Government. Nyla embraces a forward-thinking and bold approach at every turn, earning us a solid reputation of technical trendsetters within the industry. We have a passion for developing solutions that have a quick and immediate impact on mission. Headquartered in Columbia, Maryland, our customers love how we tackle their most challenging problems and get things done. If you have the unique experience and expertise we are seeking, along with the desire and determination to invest your time and energy as a part of Nyla's team, Taking Care of All of You Nyla provides a top-of-market compensation and benefits package. And through our unique Nyla FLEX program, we custom tailor these benefits to best fit your lifestyle. The Nyla FLEX benefit program is designed to offer you flexibility in the 3 biggest areas of your life: your pay, your leave, and your schedule. PAY - Nyla starts with 4 weeks of Annual Leave plus 11 holidays and an additional day of Annual Leave for each year you're at the company. You have the flexibility to cash out your annual leave hours, opt out of other Nyla benefits, and/or arrange for additional hours on contract (over 40 hrs/week). There's even an option to earn 1.3 times your hourly rate once you work over 1880 hours on contract! LEAVE - Want to spend more time with the family? Want more time to travel the world? You can BUY additional annual leave for a total of 6 weeks of annual leave. That's up to 240 hours of leave plus 11 holidays! That's not even including paid anniversary leave! SCHEDULE - Does the traditional 40-hour workweek no longer fit your lifestyle? With Nyla FLEX, you have the freedom to scale down to 30-32 hours while still enjoying the top-notch Nyla benefits you know and love. It's flexibility that works for you without compromising the perks! WHAT ABOUT OTHER BENEFITS? Nyla's health care (medical, dental, and vision) is 100% covered by the company. We provide 10% 401k matching - with full vesting day 1! Our Professional Development offers $5,000 per year to be used towards fees, tuition, or time off for your continued growth. We even have a student loan repayment program and we provide 8 hours of volunteering annually so you can support your community, making your world a better place. To learn more about Nyla's culture and our exceptional benefit packages click here. Nyla is an equal opportunity employer.

XYZ Tech Inc. seeks a Temporary Holiday Sierra professional to support software and data projects during peak season. Working with our Chicago-based team, you'll assist in developing, testing, and deploying innovative applications and data solutions that enhance client operations. Responsibilities include configuring software, troubleshooting issues, documenting processes, and collaborating with engineers and analysts. You'll gain exposure to cutting-edge tools, agile practices, and cloud platforms while contributing to real client deliverables in an inclusive, fast-paced environment that values learning, innovation, and teamwork. Responsibilities Support development, testing, and deployment of software and data solutions during peak holiday period Troubleshoot application issues and escalate complex problems to senior engineers Write and maintain technical and user documentation for features and processes Execute test cases, log defects, and assist in quality assurance activities Collaborate with developers, data analysts, and project managers in an agile environment Assist with configuration, setup, and basic administration of cloud-based applications Prepare basic data queries and reports to support client deliverables Use issue-tracking and version-control tools to manage work items and code changes Participate in daily stand-ups and sprint reviews to report progress Follow security and compliance guidelines when handling client data and systems Required Skills Software troubleshooting Basic programming (Python/Java/C#) SQL and database querying Data analysis and reporting Version control (Git) Agile/Scrum methodologies Cloud platforms (AWS/Azure/GCP) Technical documentation Issue tracking (Jira or similar) Software testing and QA

Who We Are Hi, we're DuckDuckGo, the online protection company and remote-first team of 300+ on a mission to raise the standard of trust online. Founded in 2008 and profitable since 2014, annual revenue now exceeds $100m USD and millions use our browser on on Mac, Windows, iOS, and Android, our search engine, and the DuckDuckGo subscription. We also offer private, useful, and optional AI, including Duck.ai, which lets you chat privately with ChatGPT, Claude, and other AIs, all in one place. Our culture of trust, inclusivity, and empowered project management underpins everything we do, where each team member takes full ownership of their projects, from scoping and execution to postmortem. If you're seeking end-to-end ownership of your work, you've come to the right place! Your Team and Role Working on the Site Reliability Team, you'll help build and maintain world-class infrastructure to meet the needs of millions of users protecting their privacy online. You'll utilize high-level languages like Perl, Go, TypeScript, or Python and work on related projects. Recent projects include: Ensuring our Duck.ai product meets our reliability standards and minimizing user friction on failures Scaling up our own index infrastructure to handle billions of documents Create anti fraud verifications that respect users privacy As Director, Site Reliability Engineering, you'll dive deep into complex operational challenges, including software, systems, automation, and process analysis. We are looking for candidates who can read, write, troubleshoot, and deploy all types of software to help us tackle the reliability challenges of large-scale deployments. About You 10+ years relevant professional experience in reliability, platform, infrastructure, or software engineering, including 4+ years leading SRE teams. Experience participating in a 24x7 on-call rotation for a large-scale deployment. Ability to lead and collaborate on high-impact and complex projects from proposal through postmortem. Proficient in AI-driven development, including designing and implementing agentic workflows Skills to wrangle vague problems, propose innovative solutions, and execute them with a strong focus on metrics. Experience developing effective tools, services, alerts, and responses to identify and address reliability risks. Investigative abilityto root-cause sources of instability in high-traffic, distributed systems. Deep experience administering and troubleshooting Linux and web technologies. Ability to implement automation around infrastructure provisioning and configuration management to prioritize efficiency, scalability, and reliability. Foresight to help identify the future technical direction of our deployment with the goal of improving reliability and performance. Advanced programming skills enabling close partnership with software engineers to triage production issues and identify appropriate remediation, including code changes and performance considerations. Ability to leverage cloud-native services and architectures to enhance reliability and scalability, with hands-on experience packaging and deploying applications using Docker and Docker Compose. Compensation $243,800 USD annually and stock options. Compensation is transparent across the organization, and all team members within the same professional level and global region receive the same compensation. Eligibility for company-sponsored health benefits is limited to team members based in the United States. This program does not extend to team members located in other countries, such as Canada or the UK. Our Team Member Support Guide explains how we prioritize your wellbeing including paid parental leave, office setup, and co-working allowances. Hiring Process Hiring works best when it's a two-way street. Learn how we help you get to know DuckDuckGo, envision your future role here, and find out more about how we hire. Diversity, Equity and Inclusion DuckDuckGo provides equal work opportunities to all team members and applicants, and it prohibits discrimination and harassment of any type on the basis of race, color, ethnicity, caste, religion, age, sex (including pregnancy), national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by our policies or federal, state, or local laws. We want to ensure that our hiring process is accessible. If you need reasonable accommodation for any part of the application process because of a medical condition or disability, please send an email to to let us know the nature of your request. Please note that: You'll be required to attend meetings on camera via video conferencing Expect to travel at least two times a year: once for our all-hands meetup and again for a team retreat (each around 4-5 days). While extenuating circumstances may impact attendance, everyone is strongly encouraged to attend. While we offer a flexible work arrangement with no core hours, expect an average full-time commitment of 40 hours per week. A successful candidate must pass a background check as a condition of joining the team. By applying for this role, you confirm that all information submitted is accurate and complete. You further acknowledge that providing false or fraudulent information during the application process is cause for denial of an offer, revocation of any existing offer, or other adverse action, up to and including termination after the start of your commencement of work. Disclosure Statement: Use of AI in Hiring Process As part of our commitment to enhancing our recruitment process, we utilize artificial intelligence (AI) technology to assist in reviewing and summarizing job applications and test projects, including those tools integrated into our recruitment vendor platforms. We use AI to flag potentially fraudulent applications, analyze and summarize applicants' experience, interviews, and project performance, and help streamline our selection process. Key Principles: Data Privacy: All information provided in your application will be handled in accordance with our Recruiting Privacy Policy. We ensure that your personal information is protected and used solely for recruitment purposes. Human Oversight and Accountability: The AI technology is designed to support our hiring team by providing insights and summaries of applications and evaluations of test projects against scoring rubrics. All final evaluations and hiring decisions, however, will be made by our hiring team, who will consider the AI's input alongside other factors. Transparency: We believe in transparency regarding our hiring practices. If you have any questions about how AI is used in our recruitment process, please feel free to reach out to us. By submitting your application, you acknowledge and consent to the use of AI technology in our review process. If you would like to request an alternative selection process, please contact us as at . Thank you for your interest in joining DuckDuckGo!

Job Description Job Description Overview WORK ENVIRONMENT The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of the job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Location: On-site, DISA Headquarters, 6914 Cooper Ave, Fort Meade, MD 20755 Type of environment: Office - on-site at a Government facility (classified environment) Noise level: Low to Medium Work schedule: Day shift Monday - Friday. May be requested to work evenings and weekends to meet program and contract needs. Amount of Travel: Less than 10%. Occasional travel within the National Capital Region and to up to four (4) conferences per year. WORK AUTHORIZATION/SECURITY CLEARANCE U.S. Citizen Secret Clearance PHYSICAL DEMANDS The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to use hands to handle, feel, touch; reach with hands and arms; talk and hear. The employee is regularly required to stand; walk; sit; climb or balance; and stoop, kneel, crouch, or crawl. The employee is regularly required to lift up to 10 pounds. The employee is frequently required to lift up to 25 pounds; and up to 50 pounds. The vision requirements include close vision, distance vision, peripheral vision, depth perception, and ability to adjust focus. WAGE INFORMATION Target salary range: $100,000 - $16,000.00/yr. The salary range displayed is an estimate only and is not a guarantee of compensation or salary and will be determined on several factors regarding the individual's particular combination of education, knowledge, skills, competencies and experience, as well as contract parameters and organizational requirements. The displayed salary is one component of the total compensation package for employees. OTHER INFORMATION Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. TekSynap is a drug-free workplace. We reserve the right to conduct drug testing in accordance with federal, state, and local laws. All employees and candidates may be subject to drug screening if deemed necessary to ensure a safe and compliant working environment. Many positions require specific certifications and/or the ability to obtain a security clearance. Security clearances may only be granted to U.S. citizens. Applicants who accept an offer of employment may be subject to investigations performed by TekSynap and/or the Government to verify the candidate meets the required qualifications and other eligibility requirements. Responsibilities We are seeking a PKI Subject Matter Expert - Cryptographic Modernization (PQC, Algorithm Evolution & NPE) - Key Personnel to join our team supporting the DISA PKI Public Key Enablement (PKE) Engineering Support Task Order (SETI Small Business) in Fort Meade, Maryland. REQUIRED QUALIFICATIONS Ten (10) or more years of hands-on PKI, cryptographic engineering, or cryptographic infrastructure experience, including at least five (5) years in DoD or federal high-security environments. Hands-on, low-level technical proficiency in cryptographic infrastructure and system integration within high-security environments, with demonstrated capability to engineer solutions for complex hardware and legacy software integrations. Demonstrated understanding of the mathematics, protocols, and hardware that drive public key cryptography. Practical, demonstrable knowledge of NIST post-quantum standards (FIPS 203 ML-KEM, FIPS 204 ML-DSA, FIPS 205 SLH-DSA) and the engineering steps to migrate production systems to quantum-safe states. Expertise automating certificate lifecycles for Non-Person Entities (routers, firewalls, microservices, Kubernetes) using automated certificate management protocols (ACME, EST). Hands-on experience with Hardware Security Modules (Entrust, Thales, SafeNet), key ceremonies, and HSM lifecycle or refresh activities. Experience with Certificate Authority platforms, preferably Red Hat Certificate System, including CA stand-up, configuration, and certificate profile management across classified and unclassified domains. Working knowledge of DoD PKI policy, DoDI 8520.02, STIG application, and the DoD PKI Authority to Operate (ATO) accreditation process. Familiarity with the DoD PKE custom tool suite (e.g., InstallRoot, FileSigner, CRL Auto Cache, PITT) and software development in C++, C#, Java, Python, or Rust is strongly preferred. Certifications Certification: DoD 8140 Cyber Workforce Qualification Program, IAT Level II, IAT Level III, IAM III or equivalent, required at time of assignment Education Education: Bachelor's degree in Computer Science, Cybersecurity, Mathematics, Engineering, or a related field. Master's degree preferred; equivalent demonstrated technical experience may be considered. Clearance Clearance: Active Secret clearance required. Must be onboard at the start of the Period of Performance. RESPONSIBILITIES Serve as the senior technical and cryptographic authority, the technical trust anchor for implementation, for algorithm evolution and Post-Quantum Cryptography across the DoD PKI Portfolio. Recommend approaches that steer the PKI program away from legacy, vulnerable cryptographic implementations. Develop and execute the plan to migrate DoD PKI to stronger cryptographic algorithms in accordance with NIST SP 800-131A Rev3 and the CNSA 2.0 PQC timeline, addressing encryption, digital signing, key agreement, key derivation, key wrapping, key transport, hash functions, and message authentication codes. Apply practical knowledge of NIST-standardized quantum-resistant algorithms, including ML-KEM and ML-DSA, and define the engineering steps required to transition existing systems to quantum-safe states. Maintain cryptographic agility across the portfolio in response to evolving NIST standards and Executive Order 14412, so algorithm changes are absorbed by configuration and re-test rather than re-architecture. Coordinate with COTS vendors (approximately 15 in the current ecosystem) to test feasibility and assess the timeliness of product transition plans; maintain the vendor tracking report and raise capability gaps to the coordination cell and Component CIOs. Develop and deploy PKE lab environments supporting Algorithm Evolution and PQC integration testing; document and report results to DoD working groups including the Certificate Validation Tiger Team. Evaluate HSM platforms for PQC readiness; HSM hardware refresh is within contract scope and the current environment uses Entrust HSMs. Provide Non-Person Entity (NPE) certificate management expertise, automating certificate lifecycles for routers, firewalls, microservices, and Kubernetes clusters using automated certificate management protocols such as EST and ACME. Support Certificate Authority Development, including CA architecture analysis, deficiency identification, Analysis of Alternatives development, and CA deployment across 42 NIPRNet and 31 SIPRNet Red Hat Certificate Authorities supporting approximately 10,000 certificate issuances per day. Provide input to CA deployment plans and develop and document Change Requests to modify software and hardware configurations, submitted through the PKI Configuration Management process and Remedy. Provide Tier III technical support for the most complex cryptographic and infrastructure issues, and review and update PKE enablement documentation as cryptographic changes are released. Qualifications TekSynap is a fast growing high-tech company that understands both the pace of technology today and the need to have a comprehensive well planned information management environment. "Technology moving at the speed of thought" embodies these principles - the need to nimbly utilize the best that information technology offers to meet the business needs of our Federal Government customers. Apply now to explore jobs with us at . We offer our full-time employees a competitive benefits package to include health, dental, vision, 401K, life insurance, short-term and long-term disability plans, vacation time and holidays. TekSynap is a drug-free workplace. We reserve the right to conduct drug testing in accordance with federal, state, and local laws. All employees and candidates may be subject to drug screening if deemed necessary to ensure a safe and compliant working environment. By applying to a role at TekSynap you are providing consent to receive text messages regarding your interview and employment status. If at any time you would like to opt out of text messaging, respond "STOP". As part of the application process, you agree that TekSynap Corporation may retain and use your name, e-mail, and contact information for purposes related to employment consideration. EQUAL EMPLOYMENT OPPORTUNITY . click apply for full job details

Machine Learning Engineer 5 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 San Francisco, CA: $250,800 - $286,200 for Machine Learning Engineer 5 San Jose, CA: $250,800 - $286,200 for Machine Learning Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

What you will do We are seeking a Manufacturing IT Platform Engineer with 8-10 years of traditional software development hands-on experience using AI-assisted development tools (e.g., Claude.ai, ChatGPT, GitHub Copilot). The role focuses on building productized, reusable capabilities for MoM/MES integration, manufacturing data platforms, and edge deployments, using containers and Kubernetes to deploy and support solutions in manufacturing plants. This role is ideal for an engineer who wants to build real systems used on the shop floor, leverage AI tools to accelerate development, and grow into a strong manufacturing platform contributor. How you will do it Design and develop services supporting MoM/MES use cases such as production events, quality data, and traceability. Build manufacturing data pipelines and APIs for real-time and near real time shop-floor data. Develop and maintain edge-deployed applications that interface with plant systems (MES, SCADA, historians, equipment data). Use AI development tools (Claude.ai, Copilot studio) for: Code generation and refactoring Test creation and debugging Documentation and design acceleration Configuration of the MOM/MES Containerize applications using Docker and deploy using Kubernetes (on prem or edge environments). Support plant deployments, validation, and basic production support. Collaborate with senior engineers and architects to improve reliability, scalability, and usability of the platform. What we look for Required 8-10 years of traditional software development experience in Manufacturing. 2-3 years of active experience using AI tools for software development. Strong coding skills in Python, Java, Node.js, or .NET. Experience building and consuming REST APIs and data services. Hands-on experience with Docker containers. Working knowledge of Kubernetes concepts (pods, deployments, services). Basic understanding of manufacturing or industrial systems (MES, shop-floor data, production systems). Preferred Exposure to MoM/MES, SCADA, Historian, or IoT systems. Experience with event-driven or streaming architectures (e.g., MQTT, pub/sub concepts). Any experience deploying applications to edge or on-prem environments. Familiarity with CI/CD pipelines and DevOps practices. Experience with manufacturing and shop-floor systems. Comfortable combining traditional engineering skills with AI-assisted development. Product-oriented mindset-focused on reusable, scalable solutions. Willingness to support plant users and learn from operational feedback. Curious, hands-on, and ownership driven. Meadowbrook - Lithium Ion Our Meadowbrook, Michigan plant produces lithium-ion batteries and runs a research lab. We began operations in 2010 and now employ more than 110 people and operate six days per week. We're mindful of the profound impact we have on our planet and are proud to operate in a LEED Gold Certified facility. Our employees are actively involved in the community and volunteer for a variety of local organizations. What you get: Medical, dental and vision care coverage and a 401(k) savings plan with company matching - all starting on date of hire Tuition reimbursement, perks, and discounts Parental and caregiver leave programs All the usual benefits such as paid time off, flexible spending, short-and long-term disability, basic life insurance, business travel insurance, and Employee Assistance Program Global market strength and worldwide market share leadership HQ location earns LEED certification for sustainability plus a full-service cafeteria and workout facility Clarios has been recognized as one of 2026's Most Ethical Companies by Ethisphere. This prestigious recognition marks the fourth consecutive year Clarios has received this distinction. Who we are: Clarios is the force behind the world's most recognizable car battery brands, powering vehicles from leading automakers like Ford, General Motors, Toyota, Honda, and Nissan. With 18,000 employees worldwide, we develop, manufacture, and distribute energy storage solutions while recovering, recycling, and reusing up to 99% of battery materials-setting the standard for sustainability in our industry. At Clarios, we're not just making batteries; we're shaping the future of sustainable transportation. Join our mission to innovate, push boundaries, and make a real impact. Discover your potential at Clarios-where your power meets endless possibilities. Veterans/Military Spouses: We value the leadership, adaptability, and technical expertise developed through military service. At Clarios, those capabilities thrive in an environment built on grit, ingenuity, and passion-where you can grow your career while helping to power progress worldwide. All qualified applicants will be considered without regard to protected characteristics. Equal Employment Opportunity: We recognize that people come with a wealth of experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please apply. Diversity of experience and skills combined with passion is key to challenging the status quo. Therefore, we encourage people from all backgrounds to apply to our positions. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, status as a protected veteran or other protected characteristics protected by law. As a federal contractor, we are committed to not discriminating against any applicant or employee based on these protected statuses. We will also take affirmative action to ensure equal employment opportunities. Please let us know if you require accommodations during the interview process by emailing . We are an Equal Opportunity Employer and value diversity in our teams in terms of work experience, area of expertise, and all characteristics protected by laws in the countries where we operate. For more information on our commitment to sustainability, diversity, and equal opportunity, please read our latest report . We want you to know your rights because EEO is the law. A Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. Clarios will never require a job applicant to pay money as part of the application or hiring process. To All Recruitment Agencies: Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.

Machine Learning Engineer 3 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 3 years of experience programming with Python, Java, Golang, or C++ At least 2 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 3 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 1 year of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 1+ years of experience optimizing ML algorithms, configurations, and infrastructure 1+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 1+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 1+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) Authored/co-authored a paper on a ML technique, model, or proof of concept 1+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Machine Learning Engineer 3 New York, NY: $176,500 - $201,400 for Machine Learning Engineer 3 Plano, TX: $147,100 - $167,900 for Machine Learning Engineer 3 Richmond, VA: $147,100 - $167,900 for Machine Learning Engineer 3 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 5 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 5 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 5 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 3 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 3 years of experience programming with Python, Java, Golang, or C++ At least 2 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 3 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 1 year of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 1+ years of experience optimizing ML algorithms, configurations, and infrastructure 1+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 1+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 1+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) Authored/co-authored a paper on a ML technique, model, or proof of concept 1+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Machine Learning Engineer 3 New York, NY: $176,500 - $201,400 for Machine Learning Engineer 3 Plano, TX: $147,100 - $167,900 for Machine Learning Engineer 3 Richmond, VA: $147,100 - $167,900 for Machine Learning Engineer 3 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).