Staff Software Engineer, ML

Apptronik•Sunnyvale, CA

10h•$285,000 - $340,000

About The Position

Apptronik is seeking a Staff MLOps Engineer to own the technical direction of its MLOps platform, which connects teleoperation data collection with deployed autonomy on the Apollo humanoid robot. The role sets the architecture for dataset lifecycle management, experiment tracking, model registry, evaluation harnesses, and model serving and packaging for deployment to robots. This is a hands-on technical leadership position rather than a management role. The engineer will contribute directly while leading by influence across MLOps, Autonomy, Data Platform, and TeleOp; establish platform standards and contracts; turn research code into repeatable, auditable pipelines; and partner closely with the Training Infrastructure engineer responsible for the training cluster.

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 end-to-end MLOps platform, including dataset lifecycle, experiment tracking, model registry, evaluation, and serving, at a company that ships models to production.
Expertise in dataset versioning (such as DVC, LakeFS, or Delta), experiment tracking (such as MLflow, W&B, or Determined), model registries, 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 regression costs are high, such as robotics, safety-critical systems, or customer-facing production systems.
Experience leading technical projects end-to-end, including architecture, implementation, validation, and iteration.
Ability to lead by influence across teams, establish standards other engineers adopt, and mentor engineers.
Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, and modern CI/CD workflows.
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

Master's degree in Computer Science, Machine Learning, or a related technical field; a Bachelor's degree may be considered with exceptional experience.
Experience deploying ML models to edge or embedded targets, including on-device inference, ONNX Runtime, TensorRT, or robot fleets.
Experience with reinforcement learning training and evaluation infrastructure for embodied agents, such as rollout workers, replay buffers, and simulation-evaluation harnesses.
Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains.
Experience with simulation-in-the-loop evaluation using 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 MLOps platform's technical direction, define subsystem interfaces, drive architecture decisions, and establish engineering standards for how datasets, experiments, and models move through company systems.
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 layer, including versioning, lineage, splits, and labeling-integration handoff.
Ensure every trained model can be traced to the exact data and code that produced it.
Build and operate a model registry for versioned artifacts, metadata, evaluation results, lineage, and approval workflows.
Define the promotion path from trained models to qualified models and deployment to robots.
Define offline benchmarks, simulation rollouts, and policy-gating harnesses that models must pass before reaching Apollo.
Develop the metrics framework used by the autonomy team to gate releases.
Own the path from registered model to running inference on Apollo, including packaging, on-robot versioning, rollback, and observability of deployed policy behavior.
Coordinate with Connect and Data Platform on deployment and fleet telemetry.
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 adopt the platform's primitives.