A
Apptronik
Senior Software Engineer (ML Infrastructure)
Skills
DockerPythonMachine LearningKubernetes
2 months ago
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Important information
Contract type:
Permanent
Location:
Austin, TX, USA
Work mode:
Onsite
Published on:
3 September 2026
What they need
- You will build the ML platform — the APIs, workers, and control planes that let researchers and robot teams move data and models through the system in a self-serve manner, with the testing and observability that being a dependency implies
- Data Curation & Annotation: Turn raw robot and simulation data into training-ready datasets — selection and filtering of manipulation episodes with synchronized sensor streams; annotation workflows that combine automatic labeling with human-in-the-loop review at throughput; and dataset versioning and lineage strong enough that any model traces back to the exact data that produced it
- Data Pipelines at Scale: Make multi-terabyte dataset operations routine — transformation and assembly, coverage and quality statistics that tell us a training set is good before we spend a cluster-week on it, and read paths that keep GPUs fed
- Simulation & Evaluation: Build the rollout harnesses that evaluate policies in simulation on our GPU cluster; the benchmarks and metrics captured consistently across simulation, real-robot, and teleoperation sources; and the qualification gates a model must pass before it reaches Apollo — automatic, not manual review
- Model Promotion: Build the model store — versioning, metadata, attached evaluation results, lineage — and the promotion path from trained to qualified to deployed on robot, including packaging (ONNX, TensorRT) in partnership with Autonomy
- Developer Experience: Provide the tooling researchers use daily — experiment tracking, training job submission, sweeps, and reproducible container environments. Reduce time from idea to running training job; win adoption by being the fastest path, not by mandate
- Alongside the technical work, you will partner with Autonomy, Data Platform, and TeleOp on dataset and model lifecycle contracts, contribute to the technical direction of these layers, and mentor the engineers around you through code and design review- No single person will have depth in everything below. We are looking for someone who has built platform services in production at scale with real depth in at least one of three areas — large-scale data pipelines, annotation and labeling, or evaluation and simulation — plus solid cloud and Python across the board
- Must be able to lift 15 pounds at times
- Strong Python and general software engineering ability (testing, API design, code review), plus cloud infrastructure, Kubernetes, Docker, and modern CI/CD
- Experience building large-scale evaluation or simulation harnesses: many parallel jobs on GPU infrastructure, aggregated into decision-grade results
- A builder at scale: a track record of designing and shipping production systems and services that other teams depend on daily
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent experience
- Prolonged periods of sitting at a desk and working on a computer
- Experience with ML annotation and labeling at scale: automatic annotation of data combined with human-in-the-loop workflows — the tooling, quality control, and throughput management
- Deep hands-on experience with large-scale data pipelines for ML: multi-terabyte transformation and dataset assembly of multimodal sensor data — video and image streams, time-synchronized robot telemetry, the kind of data that trains vision-language-action and computer-vision models — with columnar and time-series formats (Parquet, Arrow), dataset versioning and lineage (lakeFS, DVC, Iceberg, or equivalent), and object storage (S3, MinIO)
- Hearing and speech to communicate
- Vision to read printed materials and a computer screen
- 5+ years of professional software engineering experience in ML platforms, data infrastructure, or related fields, OR 3+ years of direct, hands-on experience owning the data and evaluation infrastructure behind models shipped to production
- Robotics data formats and fleet-scale telemetry (MCAP, ROS, LeRobot, or equivalent)
- Simulation-in-the-loop evaluation with Isaac Sim, IsaacLab, MuJoCo, or equivalent
- Reinforcement or imitation learning infrastructure for embodied agents (rollout workers, sim-eval harnesses)
- Deploying ML models to edge targets (ONNX Runtime, TensorRT, robot fleets)
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