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
This is a direct hire. Please, no outside Agency solicitations.
Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
