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Staff MLOps Engineer

Apptronik· Software Engineering
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Austin, TXfull-timePosted 3mo ago

About the role

Staff MLOps Engineer responsible for the technical direction and end-to-end ownership of Apptronik's MLOps platform, bridging data collection, model development, evaluation, and on-robot deployment for Apollo. Hands-on technical leadership with mentoring, collaborating with Autonomy, Data Platform, TeleOp, and Training Infrastructure.

Responsibilities

  • Own the technical direction for the MLOps platform — define subsystem interfaces, drive architecture decisions, and establish engineering standards for datasets, experiments, and models.
  • 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 end-to-end — versioning, lineage, splits, and labeling-integration handoff.
  • Ensure every trained model can be traced back to the exact data and code that produced it.
  • Build and operate a first-class model registry — versioned artifacts, metadata, evaluation results, lineage, and approval workflows.
  • Define the promotion path from trained to qualified to deployed to robot.
  • Define offline benchmarks, simulation rollouts, and policy-gating harnesses for model qualification.
  • Develop the metrics framework trusted by the autonomy team to gate releases.
  • Own the path from registered model to running inference on Apollo — packaging (ONNX, TensorRT, torch.compile), on-robot versioning, rollback, and observability.
  • Coordinate with Connect and Data Platform on the deploy-and-telemetry seam for fleet deployments.
  • Mentor mid-level and senior engineers on the MLOps team through code/design reviews and collaboration.
  • Influence research workflows across Autonomy to standardize platform primitives and collaborate with Training Infrastructure on cluster/platform contracts.

Qualifications

  • Proficiency in Python and at least one systems language (Go, Rust, or C++) with ability to justify architectural tradeoffs in production ML platforms.
  • Experience owning and delivering an MLOps platform end-to-end (dataset lifecycle, experiment tracking, model registry, evaluation, serving) in production.
  • Expertise in dataset versioning (e.g., DVC, LakeFS, Delta), experiment tracking (MLflow, W&B, Determined), model registry, and policy serving.
  • Strong background in designing service-oriented systems on Kubernetes and API-to-infrastructure contracts.
  • Experience defining evaluation/qualification frameworks for ML models where regression costs are high (robotics, safety-critical, or production-facing).
  • Experience leading technical projects end-to-end (architecture, implementation, validation, iteration).
  • Proven ability to lead by influence across teams and mentor engineers.
  • Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, and modern CI/CD workflows.

Skills

Required:PythonGo, Rust, or C++ML lifecycle toolingKubernetesCloud platformsCI/CD

Benefits

  • Competitive salary commensurate with experience
  • Health, dental, and vision insurance
  • 401(k) with company contributions
  • Paid time off and holidays
  • Onsite amenities and relocation support for Austin, TX
Last updated Aug 9, 2026. Interested? Apply on the company site.

About Apptronik

One Platform for the Future of Work

Apptronik designs humanoid robotics platforms with mobility, manipulation, and human-centered interaction to support people in the workplace.

3PLApolloApollo for ManufacturingApollo for RetailApollo for WarehouseBuilt for the Real WorldForm That Fits the WorkGoods-to-Person
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