Evaluation & Qualification Harnesses

• Automated Evaluation: Define the offline benchmarks, simulation rollouts, and policy-gating harnesses that any model must pass before reaching Apollo.
• Metrics Framework: Develop the metrics framework that the autonomy team trusts to gate releases.

Serving, Packaging & Deployment to Robot

• On-Robot Path: Own the path from registered model to running inference on Apollo — packaging (ONNX, TensorRT, torch.compile), versioning on-robot, rollback, and observability of deployed policy behavior.
• Telemetry Seam: Coordinate with Connect and Data Platform on the deploy-and-telemetry seam back from the fleet.

Mentorship & Cross-Functional Leadership

• Mentorship: Mentor mid-level and senior engineers on the MLOps team through code review, design review, and direct collaboration.
• Influence: Partner with the Training Infrastructure engineer on the cluster/platform contract, and influence research workflows across Autonomy to standardize on the platform's primitives.

SKILLS AND REQUIREMENTS

• Deep proficiency in Python and at least one systems-level language (Go, Rust, or C++), with demonstrated ability to make and defend architectural tradeoffs in production ML platforms
• Proven experience owning and delivering an MLOps platform end-to-end — dataset lifecycle, experiment tracking, model registry, evaluation, and serving — at a company that ships models to production
• Expertise across the model lifecycle: dataset versioning (DVC, LakeFS, Delta, or equivalent), experiment tracking (MLflow, W&B, Determined), model registry, and policy serving
• Strong background designing service-oriented systems on Kubernetes; comfortable with the contract between platform APIs and underlying compute infrastructure
• Experience defining evaluation and qualification frameworks for ML models where the cost of a regression is high (robotics, safety-critical, or production-customer-facing)
• Experience leading technical projects end-to-end: architecture, implementation, validation, and iteration
• Demonstrated ability to lead by influence across teams — setting standards that other engineers adopt voluntarily, and mentoring engineers around you
• Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, and modern CI/CD workflows

EDUCATION and/or EXPERIENCE

• Master's degree in Computer Science, Machine Learning, or a related technical field preferred; Bachelor's considered with exceptional experience.
• 8+ years of professional software engineering experience in ML platforms or related infrastructure, OR 4+ years of direct, hands-on experience owning an MLOps platform that shipped models to production.

Preferred Qualifications:

• Experience deploying ML models to edge or embedded targets (on-device inference, ONNX Runtime, TensorRT, robot fleets)
• Experience with RL training and evaluation infrastructure for embodied agents (rollout workers, replay buffers, sim-eval harnesses)
• Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains
• Experience with simulation-in-the-loop evaluation (IsaacSim, MuJoCo, or equivalent)
• Familiarity with policy gating, shadow deployments, or staged rollout strategies for autonomy
• Open-source contributions to MLOps platform tooling (MLflow, BentoML, KServe, Ray Serve, etc.)

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate

*This is a direct hire. Please, no outside Agency solicitations.

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