Senior Software Engineer (ML Infrastructure)

• We’re hiring a strong software engineer to own the ML Infrastructure that powers how Voxel trains and ships vision models
• You’ll build systems that let our applied ML team train multiple models concurrently, manage experiments and ship optimized models to production
• You’ll set technical direction, write code, make architecture calls, and partner closely with applied CV, ML Data and Platform engineers
• Build and maintain training infrastructure that lets the applied ML team train multiple models concurrently, manage experiments, and iterate quickly on new architectures
• 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
• Establish ML experiment tracking and lifecycle management - pick the right tools (Weights & Biases, MLflow, ClearML, or similar) so researchers can run, compare, and reproduce experiments efficiently
• Establish DevOps-for-ML best practices on AWS (IaC, CI/CD, observability, cost monitoring) so researchers can iterate quickly and safely
• Understand the infra needs of applied ML/CV engineers and design scalable solutions that support model development

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- Bias toward shipping. You’d rather ship something good this week than something perfect next quarter
• 4+ years of experience building and shipping large scale software solutions
• Track record of owning infrastructure end-to-end: scoping, building, shipping, and improving systems that internal teams depend on
• Strong communication skills
• Hands-on experience building ML training pipelines in PyTorch
• Experience with AWS (S3, EC2, EKS, or similar) for ML workloads
• Strong Python. Write performant code that scales well in production environments
• Hands-on experience with ML experiment tracking and lifecycle tools (Weights & Biases, MLflow, ClearML, or similar)
• Familiarity with GPU performance profiling and optimization (Nsight, PyTorch profiler, or similar)
• Background in computer vision model training
• Experience with modern ML orchestration tools (Ray, Sematic, Flyte, Metaflow, Prefect, or similar)