Senior Software Engineer, ML Infrastructure

About the role

Apptronik is a human‑centered robotics company developing AI‑powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems.

You will join a team dedicated to bringing Apollo to market at scale, tackling complex challenges like safety, commercialization, and mass production to change the world for the better.

Job Summary: Apptronik is building Apollo, a general‑purpose humanoid robot, and the physical AI that drives it. Scale is the name of the game: every robot and teleoperator we field produces synchronized video, proprioceptive, tactile, and force‑torque streams, and the fleet's output grows with every deployment. Turning that volume of data into shipped autonomy — routinely, at multi‑terabyte scale — is what this role is about. We are looking for a Senior Software Engineer, ML Infrastructure to build that platform: the self‑serve services and pipelines that carry data from collection through curation, training, and evaluation to a qualified model running on real hardware.

Responsibilities

• 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 testing and observability.
• Data Curation & Annotation: Turn raw robot and simulation data into training‑ready datasets, manage selection, filtering, annotation workflows, and dataset versioning/lineage.
• Data Pipelines at Scale: Enable routine multi‑terabyte dataset operations, including transformation, assembly, quality statistics, and efficient read paths for GPU training.
• Simulation & Evaluation: Build rollout harnesses for policy evaluation in simulation on GPU clusters, capture benchmarks and metrics across simulation, real‑robot, and teleoperation, and implement automatic qualification gates for models.
• Model Promotion: Develop the model store with versioning, metadata, evaluation results, and lineage; create the promotion path from trained to qualified to deployed on robot, including packaging (ONNX, TensorRT) in partnership with Autonomy.
• Developer Experience: Provide tooling for experiment tracking, training job submission, sweeps, and reproducible container environments to reduce time from idea to training job.
• Partner with Autonomy, Data Platform, and TeleOp on dataset and model lifecycle contracts, contribute to technical direction, and mentor engineers through code and design review.

Requirements

• 5+ years of professional software engineering experience in ML platforms, data infrastructure, or related fields, or 3+ years of direct hands‑on experience owning data and evaluation infrastructure behind production models.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent experience.
• Proven ability to design and ship production systems and services used daily by other teams.
• Deep experience with large‑scale data pipelines for ML, including multi‑terabyte transformation of multimodal sensor data, columnar/time‑series formats (Parquet, Arrow), dataset versioning and lineage (lakeFS, DVC, Iceberg, or equivalent), and object storage (S3, MinIO).
• Experience with ML annotation and labeling at scale, including automatic annotation combined with human‑in‑the‑loop workflows.
• Experience building large‑scale evaluation or simulation harnesses on GPU infrastructure.
• Strong Python skills and general software engineering abilities (testing, API design, code review).
• Experience with cloud infrastructure, Kubernetes, Docker, and modern CI/CD pipelines.

Bonus Qualifications

• Familiarity with robotics data formats and fleet‑scale telemetry (MCAP, ROS, LeRobot, or equivalent).
• Experience with simulation‑in‑the‑loop evaluation using Isaac Sim, IsaacLab, MuJoCo, or equivalent.
• Background in reinforcement or imitation learning infrastructure for embodied agents (rollout workers, sim‑eval harnesses).
• Experience deploying ML models to edge targets (ONNX Runtime, TensorRT, robot fleets).

Physical Requirements

• Prolonged periods of sitting at a desk and working on a computer.
• Ability to lift up to 15 pounds occasionally.
• Vision sufficient to read printed materials and a computer screen.
• Hearing and speech to communicate.