JOB SUMMARY

Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform — the system of record for datasets, experiments, model artifacts, and serving paths that connects teleoperation data collection on one side to deployed autonomy on Apollo on the other. In this role, you will set the architecture for the platform layer above the training cluster: dataset lifecycle, experiment tracking, model registry, evaluation harnesses, and the serving / packaging path that delivers trained policies to robots in the field. You will lead by influence across MLOps, Autonomy, Data Platform, and TeleOp — establishing the standards, contracts, and tooling that turn one-off research code into a repeatable, auditable pipeline from data to deployed model. This is a hands-on technical leadership role, not a management position; you will be a primary contributor while mentoring the engineers around you, and partnering closely with the Training Infrastructure engineer who owns the cluster layer beneath the platform.

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.)

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

The annual salary range is $285,000 - $340,000