Visa
Austin, Texas
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Role Summary: The Sr. ML Engineer is responsible for building and maintaining ML platform infrastructure that powers AI/ML applications across the organization. This role is suited for a hands-on engineer with practical experience in AWS, SageMaker, Kubernetes, GPU orchestration, Infrastructure as Code, and MLOps, with a passion for building scalable, secure, and reliable platforms. The position requires an experienced ML platform engineer who can design cloud and on-prem infrastructure, manage model deployment environments, modernize legacy ML pipelines, and enable Data Scientists and AI Engineers to move models from research to production. The team is tasked with building scalable ML infrastructure and platform tooling, and the successful candidate will contribute to architectural decisions, implementation standards, and best practices across the ML platform. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools, Microsoft Copilot, ChatGPT, GitHub Copilot, and other AI-enabled productivity platforms to support everyday work. Key Responsibilities: Lead and deliver specific platform engineering deliverables as a Sr. ML Engineer. Provide guidance to the engineering team on building scalable ML infrastructure, deployment patterns, and platform capabilities. Improve the productivity of Data Scientists and AI Engineers by developing tooling that simplifies model deployment and productionization. Act as a platform design authority and shape best practices and methodologies within the ML platform team. Design and build scalable ML pipelines, orchestration frameworks, and model serving infrastructure. Collaborate with Data Scientists, AI Engineers, infrastructure teams, and security partners to integrate AI/ML solutions into production systems. Build and operate secure cloud and on-prem infrastructure using AWS, Kubernetes, SageMaker, Terraform, and related platform technologies. Support GPU-enabled infrastructure and serving frameworks for AI/ML, GenAI, and LLM workloads. Modernize legacy ML pipelines and adopt emerging technologies to improve reliability, scalability, and operational efficiency. Communicate technical concepts, platform capabilities, and architectural decisions to technical and non-technical stakeholders. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience Preferred Qualifications: Specialist: 4 or more years of relevant work experience. Experience designing, building, and maintaining scalable ML platform infrastructure for AI/ML applications. Experience with AWS services such as EC2, S3, EKS, SageMaker, IAM, VPC, and CloudWatch. Experience managing Kubernetes clusters and containerized ML workloads using Docker. Experience with ML pipeline and orchestration tools such as Kubeflow, Airflow, MLflow, or similar platforms. Experience building infrastructure automation using Terraform, CloudFormation, or other Infrastructure as Code tools. Experience developing CI/CD pipelines for ML model deployment and infrastructure changes. Experience implementing secure cloud architectures using IAM roles, VPCs, least-privilege access, and secure networking patterns. Experience with Python and shell scripting for automation, tooling, and platform operations. Experience collaborating with Data Scientists, AI Engineers, and cross-functional teams to move models from research to production. Experience with generative AI, large language models, LLMOps, or GenAI infrastructure. Experience with GPU orchestration for ML training, inference, capacity management, and workload optimization. Experience with ML serving frameworks such as vLLM, TensorRT-LLM, KServe, Triton, or similar technologies. Experience building and operating hybrid cloud or on-prem/cloud ML infrastructure. Experience with distributed computing frameworks such as Spark or distributed ML workloads. Experience improving infrastructure productivity using AI-assisted tools such as GitHub Copilot, ChatGPT, or similar tools. Experience developing robust, secure, and scalable platforms in enterprise or regulated environments. Experience conducting research, experimentation, or proof-of-concept work with emerging AI/ML infrastructure technologies. Experience mentoring junior engineers and leading implementation of key platform modules. Information for US Applicants For roles located in the US, the estimated salary range for this position is $123,400.00 to $ 191,100.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Role Summary: The Sr. ML Engineer is responsible for building and maintaining ML platform infrastructure that powers AI/ML applications across the organization. This role is suited for a hands-on engineer with practical experience in AWS, SageMaker, Kubernetes, GPU orchestration, Infrastructure as Code, and MLOps, with a passion for building scalable, secure, and reliable platforms. The position requires an experienced ML platform engineer who can design cloud and on-prem infrastructure, manage model deployment environments, modernize legacy ML pipelines, and enable Data Scientists and AI Engineers to move models from research to production. The team is tasked with building scalable ML infrastructure and platform tooling, and the successful candidate will contribute to architectural decisions, implementation standards, and best practices across the ML platform. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools, Microsoft Copilot, ChatGPT, GitHub Copilot, and other AI-enabled productivity platforms to support everyday work. Key Responsibilities: Lead and deliver specific platform engineering deliverables as a Sr. ML Engineer. Provide guidance to the engineering team on building scalable ML infrastructure, deployment patterns, and platform capabilities. Improve the productivity of Data Scientists and AI Engineers by developing tooling that simplifies model deployment and productionization. Act as a platform design authority and shape best practices and methodologies within the ML platform team. Design and build scalable ML pipelines, orchestration frameworks, and model serving infrastructure. Collaborate with Data Scientists, AI Engineers, infrastructure teams, and security partners to integrate AI/ML solutions into production systems. Build and operate secure cloud and on-prem infrastructure using AWS, Kubernetes, SageMaker, Terraform, and related platform technologies. Support GPU-enabled infrastructure and serving frameworks for AI/ML, GenAI, and LLM workloads. Modernize legacy ML pipelines and adopt emerging technologies to improve reliability, scalability, and operational efficiency. Communicate technical concepts, platform capabilities, and architectural decisions to technical and non-technical stakeholders. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience Preferred Qualifications: Specialist: 4 or more years of relevant work experience. Experience designing, building, and maintaining scalable ML platform infrastructure for AI/ML applications. Experience with AWS services such as EC2, S3, EKS, SageMaker, IAM, VPC, and CloudWatch. Experience managing Kubernetes clusters and containerized ML workloads using Docker. Experience with ML pipeline and orchestration tools such as Kubeflow, Airflow, MLflow, or similar platforms. Experience building infrastructure automation using Terraform, CloudFormation, or other Infrastructure as Code tools. Experience developing CI/CD pipelines for ML model deployment and infrastructure changes. Experience implementing secure cloud architectures using IAM roles, VPCs, least-privilege access, and secure networking patterns. Experience with Python and shell scripting for automation, tooling, and platform operations. Experience collaborating with Data Scientists, AI Engineers, and cross-functional teams to move models from research to production. Experience with generative AI, large language models, LLMOps, or GenAI infrastructure. Experience with GPU orchestration for ML training, inference, capacity management, and workload optimization. Experience with ML serving frameworks such as vLLM, TensorRT-LLM, KServe, Triton, or similar technologies. Experience building and operating hybrid cloud or on-prem/cloud ML infrastructure. Experience with distributed computing frameworks such as Spark or distributed ML workloads. Experience improving infrastructure productivity using AI-assisted tools such as GitHub Copilot, ChatGPT, or similar tools. Experience developing robust, secure, and scalable platforms in enterprise or regulated environments. Experience conducting research, experimentation, or proof-of-concept work with emerging AI/ML infrastructure technologies. Experience mentoring junior engineers and leading implementation of key platform modules. Information for US Applicants For roles located in the US, the estimated salary range for this position is $123,400.00 to $ 191,100.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Waymo
Mountain View, California
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver -to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Team & Mission: In the Oracle Perception team, our mission is to build the ultimate cognitive engine for autonomous driving. We are pioneering the use of large multimodal foundation models (e.g., Gemini) to build a powerful offboard reasoning and data flywheel system. We are moving beyond traditional perception to true scene understanding and driving actions-building offboard models that can comprehend complex driving problems, predict object/scene dynamics, and deduce driving paths with logical rationale. Our core focus is advancing the VLM foundation itself. By pushing the boundaries of multimodal pre-training and state-of-the-art post-training (SFT, RL) , we are creating models capable of rich, reasoning-based autolabeling at a massive scale. This closed-loop data engine directly powers the training and evolution of Waymo's real-time onboard models. If you are passionate about defining VLM training recipes, scaling laws, and unlocking complex reasoning via RL, this is your opportunity to redefine the foundation of autonomous driving. In this hybrid role, you will report to a Senior Staff Technical Lead Manager. You Will: Drive Pre-training & Domain Adaptation: Lead the technical strategy for curating and constructing massive-scale, high-quality multimodal pre-training datasets. Define data mixture strategies to instill deep, Waymo-specific driving intuition and physics-grounded understanding into foundation models without catastrophic forgetting. Lead Post-Training & Reasoning Enhancement: Design and implement state-of-the-art fine-tuning (SFT) and Reinforcement Learning (RLHF/RLAIF, DPO/GRPO/PPO) pipelines. Drastically improve the model's instruction-following and complex reasoning capabilities (e.g., Chain-of-Thought, spatial-temporal reasoning, and driving rationale prediction). Pioneer the VLM Data Flywheel: Architect the highly scalable inference and evaluation pipelines that leverage these trained Gemini-class models to autonomously source, sample, and autolabel critical edge cases, directly accelerating the onboard perception models. Define Training Recipes & Scaling Laws: Conduct rigorous ablation studies to optimize model architectures, token budgets, and loss functions. Establish best practices for scaling multimodal training efficiently on large GPU/TPU clusters. Drive Cross-Functional AI Strategy: Act as the principal technical visionary across ML Infra, Perception, Behavior, and AI Foundation teams. Drive consensus on the data flywheel architecture and embed VLM reasoning capabilities seamlessly into the broader autonomous vehicle stack. Provide Staff-Level Technical Leadership: Own the long-term technical roadmap for foundation model development. Mentor senior engineers, lead rigorous design reviews, and establish standard-setting engineering practices from advanced prototyping to production deployment. You Have: Master's degree in Computer Science, AI, ML, or a related technical field. 8+ years of hands-on experience designing, training, and scaling deep learning models, with at least 3+ years focused deeply on training Large Language Models (LLMs) or Vision-Language Models (VLMs) . Proven expertise in the full lifecycle of Foundation Models: from pre-training data curation (interleaved formats, tokenization) and distributed training to advanced post-training techniques. Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably. Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems. Proven ability to operate with high ambiguity, define technical roadmaps, and drive complex, multi-quarter technical initiatives across multiple teams in a fast-paced environment. We Prefer: PhD in Computer Science, Artificial Intelligence, or a related field. Strong publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) focusing on foundation models, large-scale training, reinforcement learning, or reasoning. Deep experience with advanced Reinforcement Learning paradigms applied to language or vision tasks ( focusing on improving System 2 thinking, logical deduction, and model alignment ). Demonstrated experience in Data Engineering for Foundation Models at the scale of billions/trillions of tokens (e.g., deduplication, quality filtering, synthetic data generation). Familiarity with the systemic challenges of multimodal perception in robotics or autonomous driving (e.g., 3D scene understanding, trajectory prediction). A proven track record of Staff-level impact: influencing product direction, pioneering zero-to-one ML architectures, and multiplying team efficiency through technical leadership. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000-$310,000 USD
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver -to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Team & Mission: In the Oracle Perception team, our mission is to build the ultimate cognitive engine for autonomous driving. We are pioneering the use of large multimodal foundation models (e.g., Gemini) to build a powerful offboard reasoning and data flywheel system. We are moving beyond traditional perception to true scene understanding and driving actions-building offboard models that can comprehend complex driving problems, predict object/scene dynamics, and deduce driving paths with logical rationale. Our core focus is advancing the VLM foundation itself. By pushing the boundaries of multimodal pre-training and state-of-the-art post-training (SFT, RL) , we are creating models capable of rich, reasoning-based autolabeling at a massive scale. This closed-loop data engine directly powers the training and evolution of Waymo's real-time onboard models. If you are passionate about defining VLM training recipes, scaling laws, and unlocking complex reasoning via RL, this is your opportunity to redefine the foundation of autonomous driving. In this hybrid role, you will report to a Senior Staff Technical Lead Manager. You Will: Drive Pre-training & Domain Adaptation: Lead the technical strategy for curating and constructing massive-scale, high-quality multimodal pre-training datasets. Define data mixture strategies to instill deep, Waymo-specific driving intuition and physics-grounded understanding into foundation models without catastrophic forgetting. Lead Post-Training & Reasoning Enhancement: Design and implement state-of-the-art fine-tuning (SFT) and Reinforcement Learning (RLHF/RLAIF, DPO/GRPO/PPO) pipelines. Drastically improve the model's instruction-following and complex reasoning capabilities (e.g., Chain-of-Thought, spatial-temporal reasoning, and driving rationale prediction). Pioneer the VLM Data Flywheel: Architect the highly scalable inference and evaluation pipelines that leverage these trained Gemini-class models to autonomously source, sample, and autolabel critical edge cases, directly accelerating the onboard perception models. Define Training Recipes & Scaling Laws: Conduct rigorous ablation studies to optimize model architectures, token budgets, and loss functions. Establish best practices for scaling multimodal training efficiently on large GPU/TPU clusters. Drive Cross-Functional AI Strategy: Act as the principal technical visionary across ML Infra, Perception, Behavior, and AI Foundation teams. Drive consensus on the data flywheel architecture and embed VLM reasoning capabilities seamlessly into the broader autonomous vehicle stack. Provide Staff-Level Technical Leadership: Own the long-term technical roadmap for foundation model development. Mentor senior engineers, lead rigorous design reviews, and establish standard-setting engineering practices from advanced prototyping to production deployment. You Have: Master's degree in Computer Science, AI, ML, or a related technical field. 8+ years of hands-on experience designing, training, and scaling deep learning models, with at least 3+ years focused deeply on training Large Language Models (LLMs) or Vision-Language Models (VLMs) . Proven expertise in the full lifecycle of Foundation Models: from pre-training data curation (interleaved formats, tokenization) and distributed training to advanced post-training techniques. Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably. Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems. Proven ability to operate with high ambiguity, define technical roadmaps, and drive complex, multi-quarter technical initiatives across multiple teams in a fast-paced environment. We Prefer: PhD in Computer Science, Artificial Intelligence, or a related field. Strong publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) focusing on foundation models, large-scale training, reinforcement learning, or reasoning. Deep experience with advanced Reinforcement Learning paradigms applied to language or vision tasks ( focusing on improving System 2 thinking, logical deduction, and model alignment ). Demonstrated experience in Data Engineering for Foundation Models at the scale of billions/trillions of tokens (e.g., deduplication, quality filtering, synthetic data generation). Familiarity with the systemic challenges of multimodal perception in robotics or autonomous driving (e.g., 3D scene understanding, trajectory prediction). A proven track record of Staff-level impact: influencing product direction, pioneering zero-to-one ML architectures, and multiplying team efficiency through technical leadership. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000-$310,000 USD