Staff Software Engineer (ML Infrastructure)

• Voxel’s perception system is the technical core of everything we ship
• Our models detect human activity, equipment interactions, environmental hazards, and operational state in real time across thousands of cameras in manufacturing, logistics, retail, and pharmaceutical environments
• Safety was our wedge; it proved our platform works
• Now customers are pulling us into operations: equipment utilization, workflow compliance, process efficiency
• Every new use case runs through the perception team
• We’re hiring a Staff Software Engineer to own ML Infrastructure at Voxel
• Our applied ML team is shipping vision models into production every week, across thousands of cameras at Fortune 500 customers, and the infrastructure underneath determines how fast we can move
• You’ll set the technical direction for how we train, track, and ship vision models, build the foundational systems that the applied ML team relies on, and shape the architectural decisions that will define our ML stack for the next several years
• This is a hands-on role
• You’ll write code, make architecture calls, and own outcomes end to end
• You’ll partner closely with applied CV engineers, the ML Data team, and the Platform team, and you’ll be the technical voice in the room when ML infrastructure tradeoffs come up
• Set the technical direction for ML infrastructure at Voxel: what we build, what we buy, and how the pieces fit together as the team and model portfolio scale
• Architect and build the training infrastructure that lets the applied ML team run multiple experiments concurrently and iterate quickly on new architectures (PyTorch, AWS)
• Own the train-to-deploy handoff: export trained models to optimized inference formats (TensorRT, ONNX), quantify accuracy and latency impact, and partner with Platform on production deployment
• Pick and roll out the experiment tracking and lifecycle stack (Weights & Biases, MLflow, ClearML, or similar) so researchers can run, compare, and reproduce experiments efficiently
• Establish DevOps-for-ML best practices (IaC, CI/CD, observability, cost monitoring) so researchers can iterate quickly and safely
• Mentor engineers across Vision & AI on ML infrastructure best practices, raising the bar for how the org thinks about training, evaluation, and deployment
• Anticipate where the infrastructure needs to be in 12 to 18 months, including the upcoming move to on-device inference, and architect for that future

Benefits

• Extensive / Generous health, dental, and vision insurance
• Highly competitive paid parental leave and support system
• Ownership in the business through an Equity Incentive Plan
• Generous paid time off and / or flexible work arrangements
• Daily meals in-office, vibrant company events, team-building
• 401K retirement plan, HSA options, pre-tax Commuter Card- Strong Python. Performant, maintainable code that holds up in production
• Strong communication skills. You can explain complex tradeoffs clearly to ML researchers, infra peers, and leadership
• Deep fluency in PyTorch and the modern ML training stack. You know what good experiment tracking looks like, what makes a training pipeline reliable at scale, and where the failure modes live
• 7+ years building and shipping large-scale software systems, with at least 3 years focused on ML infrastructure or large-scale data infrastructure
• A track record of being the person who decides the architecture, not just the person who implements it. You’ve owned tool selection, framework choices, and build-vs-buy calls for systems other engineers depend on
• A pragmatic shipping orientation. You can tell the difference between architectural decisions that need to be right and ones that can be revisited later, and you don’t over-engineer the latter
• Production experience on AWS (S3, EC2, EKS, or similar) for ML workloads
• Hands-on experience with model export and inference optimization (TensorRT, ONNX, or similar), including measuring accuracy and latency tradeoffs against training-time baselines
• Experience with modern ML orchestration tools (Ray, Sematic, Flyte, Metaflow, Prefect, or similar)
• Background in computer vision model training
• Familiarity with GPU performance profiling and optimization (Nsight, PyTorch profiler, or similar)