Mlops Engineer Jobs

About The Position

Requirements

• Deep proficiency in Python and at least one systems-level language: Go, Rust, or C++.
• Demonstrated ability to make and defend architectural tradeoffs in production ML platforms.
• Proven experience owning and delivering an MLOps platform end-to-end, including dataset lifecycle, experiment tracking, model registry, evaluation, and serving, at a company that ships models to production.
• Expertise across the model lifecycle, including dataset versioning, experiment tracking, model registry, and policy serving.
• Strong background designing service-oriented systems on Kubernetes and understanding the contract between platform APIs and underlying compute infrastructure.
• Experience defining evaluation and qualification frameworks for ML models where regressions are costly, such as robotics, safety-critical, or production-customer-facing environments.
• Experience leading technical projects end-to-end, including architecture, implementation, validation, and iteration.
• Demonstrated ability to lead by influence across teams, establish standards other engineers adopt, and mentor engineers.
• Proficiency with cloud infrastructure such as AWS, GCP, or Azure, Docker, Git, and modern CI/CD workflows.
• Master's degree in Computer Science, Machine Learning, or a related technical field preferred; a bachelor's degree may be considered with exceptional experience.
• At least 8 years of professional software engineering experience in ML platforms or related infrastructure, or at least 4 years of direct, hands-on experience owning an MLOps platform that shipped models to production.

Nice To Haves

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

Responsibilities

• Own the technical direction and architecture of the MLOps platform, including subsystem interfaces, architecture decisions, and engineering standards for dataset, experiment, and model workflows.
• Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on model lifecycle and platform contracts.
• Design and operate the dataset lifecycle, including versioning, lineage, splits, and labeling-integration handoff.
• Ensure trained models are traceable to the exact data and code used to produce them.
• Build and operate a model registry with versioned artifacts, metadata, evaluation results, lineage, and approval workflows.
• Define the promotion path for models from trained to qualified to deployed on a robot.
• Define offline benchmarks, simulation rollouts, and policy-gating harnesses that models must pass before deployment to Apollo.
• Develop metrics frameworks used by the autonomy team to gate releases.
• Own the path from registered model to on-robot inference, including packaging, on-robot versioning, rollback, and observability of deployed policy behavior.
• Coordinate with Connect and Data Platform on the deployment and telemetry connection back from the fleet.
• Mentor mid-level and senior MLOps engineers through code reviews, design reviews, and direct collaboration.
• Partner with the Training Infrastructure engineer on the cluster/platform contract and influence Autonomy research workflows to standardize on platform primitives.