Staff MLOps Engineer

ESSENTIAL DUTIES AND RESPONSIBILITIES

• Platform Architecture & Ownership
• Technical Direction: Own the technical direction for the MLOps platform — define subsystem interfaces, drive architecture decisions, and establish engineering standards for how datasets, experiments, and models move through Apptronik's systems.
• Cross-Team Authority: Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on all matters of model lifecycle and platform contracts.
• Dataset Lifecycle & Versioning
• Versioning & Lineage: Design and operate the dataset layer end-to-end — versioning, lineage, splits, and labeling-integration handoff.
• Reproducibility: Ensure every trained model can be traced back to the exact data and code that produced it.
• Model Registry & Artifact Management
• Registry: Build and operate a first-class model registry — versioned artifacts, metadata, evaluation results, lineage, and approval workflows.
• Promotion Path: Define the promotion path from "trained" to "qualified" to "deployed to robot."
• 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.)