3 jobs found

Job Description Job Description About ChronoScale ChronoScale builds and runs GPU clouds for hyperscalers, AI clouds and inference companies. Over the next year we bring online a 2,048-GPU B300 cluster and a 25,000-GPU GB300 NVL72 build, and about 150 MW more capacity across Texas and North Dakota. NCPE builds, runs and supports all of it: DC Ops, network, SRE, NOC and platform software. Networking is split into two teams: Network Architecture defines the designs and standards. Network Engineering, this team, turns them into running networks at every site and keeps them running. The role You join the founding core of Network Engineering. It is a senior, hands-on role in delivery, operations and automation. You: turn reference designs into running fabrics; write the automation that deploys and checks them; are the final escalation point when a training job slows down because of the network. You work across every customer and site, not on a single project. You work closely with Network Architecture, DC Ops, SRE, the platform team and the NOC, and directly with customer engineering teams during onboarding. The networks you'll own Network What it is Compute (back-end) Rail-optimized, non-blocking InfiniBand (XDR) and RoCE fabrics on NVIDIA Spectrum-X, connecting thousands of GPUs for distributed training and inference Front-end and tenant EVPN-VXLAN fabrics under SDN control, with per-tenant VRFs from the DPU up to the firewall, and hyperscaler connections Storage Lossless, RDMA-capable networks feeding VAST, Weka and other parallel file systems at line rate Edge, interconnect and management 800G edge routing over long-haul waves with MACsec, IP transit, per-tenant NAT and IPsec, and the management, out-of-band and console networks that stay up when everything else is down How we work This is the most important part of the description. Source of truth first. Devices, cables, addresses and VLANs live in NetBox and Infrahub. Configuration, cable schedules and customer inventory are generated from that data, never typed. Every change is a pull request. It is reviewed, tested in a lab (containerlab, the SDN vendor's simulator, our own virtual and hardware labs), and deployed by tooling. The CLI is for reading state and for break-glass, not for configuring. AI tools are part of the toolchain. Claude Code, Copilot, Cursor or similar draft our templates, parsers, tests and change procedures, and review diffs. You use them every day, and you are accountable for what they produce. Responsibilities: Deliver new sites and phases. Turn reference designs into build plans, cabling and optics schedules and cutover steps. Coordinate them with DC Ops and vendors on site. Bring up fabrics. Zero-touch provisioning, onboarding into the SDN controller and NetBox, and image management. Reconcile discovered state (LLDP, serial numbers, MACs) against the cable plan until no discrepancies remain. Tune for AI workloads. Adaptive routing, congestion control (ECN, PFC, DCQCN), QoS, and subnet manager or UFM policy, all applied from templates, so collectives reach the expected bus bandwidth. Isolate tenants with EVPN-VXLAN, VRFs, InfiniBand partition keys, firewall virtual routers and ACLs, so customers share infrastructure without sharing traffic. Automate everything. Python, Jinja, Ansible and OpenTofu, plus API integrations with the SDN controller, NetBox, UFM and gNMI, for provisioning, config generation, compliance checks and change pipelines. Write the validation that proves a build, as code: The output is the evidence customers require at acceptance. links and LLDP against the cable plan; BGP sessions against what's expected; optics and light levels; switch-pair latency; fabric bandwidth, NCCL all-reduce, perftest and ib_write_bw against contracted benchmarks. Instrument the fabric with streaming telemetry into Prometheus and Grafana. Alert on link flaps, symbol and bit errors, congestion and optic degradation before customers feel them. Lead incident response as the top escalation for the NOC. Run root-cause analysis with NVIDIA, Arista, Juniper and optics vendors. Write the runbooks and standard changes that let the NOC resolve the next one without you. Close the loop with Architecture. Bring production data and lessons from every build back to Network Architecture, and keep as-built records in the source of truth, so the tenth site comes up faster than the first. Minimum Qualifications: 6+ years in network engineering, including deploying and operating production data centre or service provider networks at scale. Strong data centre routing and switching: BGP (including unnumbered and large-scale ECMP), EVPN-VXLAN and Clos fabrics. Deep enough to tell when generated configuration is wrong. At least one GPU or HPC fabric in production: You know how to find why NCCL bandwidth is low. RoCE on lossless Ethernet (PFC, ECN or DCQCN, buffer tuning, Spectrum-X or similar); or InfiniBand (UFM or OpenSM, ibdiagnet, ibstat, perfquery). Multi-tenant routing: VRFs, route targets and leaking, NAT, firewall virtual routers. Automation you have shipped and still maintain: Python you wrote, a Git workflow, CI, and network APIs. Daily use of AI coding assistants on real work. Be ready to show us where they helped and where they got it wrong. Linux networking (iproute2, ethtool, tcpdump, iperf3) and the host side of RDMA: NICs, DPUs, drivers, MTU and queue tuning. Physical-layer judgement: 400G and 800G optics, DACs and AOCs, structured cabling, and reading DOM and BER data to find the bad link. A record of large network builds, and calm recovery during P1 incidents. Preferred Qualifications: Both RoCE and InfiniBand, including fat-tree routing and UFM operations. GB300 or GB200 NVL72, B300 or H100/H200 clusters in production. BlueField DPUs in DPU mode as routers and EVPN VTEPs; DOCA, SR-IOV. Netris or another SDN controller; vCluster or vMetal; Tailscale run as infrastructure (subnet routers, ACLs in Git). NVIDIA Cumulus (NVUE) or SONiC on whitebox switches; Arista EOS; Juniper MX, SRX (Multinode HA) and EX. Data modelling in NetBox or Infrahub; OpenTofu providers; KQL. MACsec, DWDM, 400ZR and long-haul waves; IP transit and peering. Time in live data halls with liquid-cooled racks. Please note that ChronoScale cannot provide employment authorization sponsorship or immigration support for this role, now or in the future. Candidates must have valid work authorization that does not require employer sponsorship. The company has reviewed this job description to ensure that essential functions and basic duties have been included. It is intended to provide guidelines for job expectations and the employee's ability to perform the position described. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate. This document does not represent a contract of employment, and the company reserves the right to change this job description and/or assign tasks for the employee to perform, as the company may deem appropriate. This job description in no way states or implies that these are the only duties to be performed by the employee(s) incumbent in this position. Employees will be required to follow any other job-related instructions and to perform any other job-related duties requested by any person authorized to give instructions or assignments. All duties and responsibilities are essential functions and requirements and are subject to possible modification to reasonably accommodate individuals with disabilities. To perform this job successfully, the incumbents acknowledge that they possess the skills, aptitudes, and abilities to perform each duty proficiently. Some requirements may exclude individuals who pose a direct threat or significant risk to the health or safety of themselves or others. This document does not create an employment contract, implied or otherwise, other than an "at will" relationship. The company is an Equal Opportunity Employer, drug

Job Description Job Description This is a remote position. Founding ML Engineer - Computer Vision (Object Detection) On behalf of an AI commerce tech startup Remote in North America Full-time The build: Our client is an early-stage commerce tech company building production computer vision for precise item identification and pricing. As founding ML engineer, you'll own the identification architecture, data strategy, evaluation standards, and production serving path - end to end, from foundation models fine-tuned toward a specific catalog. The problem is fine-grained: brand, model, edition, variant. "Close" isn't good enough. What you'll do Design and own the CV architecture for fine-grained item identification - starting from foundation vision models, fine-tuning toward the company's catalog. Define what "accurate enough" means per category; build calibrated confidence scoring so the product says "we're not sure" instead of guessing. Build the feedback loop between model errors and what gets labeled next, with the labeling lead. Decide where to invest: broader category coverage vs. deeper accuracy on today's categories. Own the serving path from research to production - latency, cost, reliability at scale. Represent the model's capabilities and limits company-wide - including with investors and customers. Requirements What we're looking for 5+ years applied CV, with at least one system shipped to production at meaningful scale. Hands-on fine-grained / instance-level classification - a problem where "close" isn't good enough. Fluency in PyTorch or TensorFlow; fine-tuning and deploying vision transformers or CNNs in production. Evaluation frameworks for vision models - you know when a model is actually getting better. Comfortable being the most senior technical voice on an open-ended problem with no playbook. Strong communication - you explain trade-offs to non-technical stakeholders. Nice to have: resale/marketplace (StockX, GOAT, Vinted, Rebag, The RealReal) or visual search (Pinterest Lens, Google Lens, Syte); active learning / human-in-the-loop pipelines; low-latency API serving at scale; first-ML-hire startup experience.

Job Description Job Description SF Bay Area, California Fully On-site We are seeking an Founding Computer Vision Engineer - Multimodal AI & VLMs to join an early-stage technology company building AI systems for real-world industrial environments. This role focuses on multimodal visual intelligence running in hardware-constrained environments and supporting complex physical workflows. The Opportunity You will own critical parts of the AI layer, building and deploying vision-language systems that reason over images and video in real customer environments. The role combines applied AI, computer vision, evaluation systems, and production engineering. Key Responsibilities Build and deploy agentic vision-language systems for real-world industrial workflows. Develop multimodal reasoning capabilities across images and video. Work with detection, segmentation, visual reasoning, VLMs, and related applied computer vision techniques. Own model orchestration and production deployment across hardware-constrained environments. Design and maintain evaluation systems including ground-truth datasets, trajectory evaluation, and regression testing. Iterate on production models based on real-world usage and measurable system performance. Required Qualifications Approximately 1 to 5 years of relevant professional experience. Hands-on experience shipping computer vision, multimodal, or vision-language systems to production. Strong practical understanding of visual AI applied to images and/or video. Experience owning technical systems end to end rather than working exclusively on research prototypes. Comfort operating in real-time, edge, hardware-constrained, or connectivity-constrained environments. Strong software engineering fundamentals and a high level of technical ownership. Candidate Profile The strongest candidates are practical builders who have shipped real systems with real users. Experience in robotics, autonomous systems, drones, wearables, real-time video, edge computing, or adjacent physical-world AI domains is highly relevant. Work Arrangement This position is fully in person in the San Francisco Bay Area. The initial working location is in Burlingame, with the team expected to transition to a San Francisco office. Work Authorization Candidates must be able to work in the United States. Certain visa sponsorship, transfer, or change-of-status scenarios may be supported for candidates already located in the United States. New petitions requiring processing from abroad are not supported at this time.