Apptronik
Robotics
StaffMLOpsEngineer
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“Staff MLOps Engineer at Apptronik. Skills: MLOps platform, technical leadership, cloud infrastructure, robotics. Own the technical direction of our MLOps platform. Set the architecture for the platform layer”
What You'll Achieve.
bringing Apollo to market at scale; turn one-off research code into a repeatable, auditable pipeline from data to deployed model
Industry & Context.
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
What They're Looking For.
Must Have
Master's degree in Computer Science, Machine Learning, or a related technical field, 8+ years of professional software engineering experience in ML platforms or related infrastructure, 4+ years of direct, hands-on experience owning an MLOps platform that shipped models to production, Platform Architecture, Experience defining evaluation and qualification frameworks for ML models, Experience leading technical projects end-to-end, Demonstrated ability to lead by influence across teams, Proficiency with cloud infrastructure (AWS, GCP, or Azure), Docker, Git, modern CI/CD workflows
Nice to Have
Bachelor's considered with exceptional experience, Experience deploying ML models to edge or embedded targets, Experience with RL training and evaluation infrastructure for embodied agents, Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains, Experience with simulation-in-the-loop evaluation, Familiarity with policy gating, shadow deployments, or staged rollout strategies for autonomy, Open-source contributions to MLOps platform tooling
What You'll Do.
Own the technical direction of our MLOps platform
Set the architecture for the platform layer
Establish the standards
Turn one-off research code into a repeatable
Be a primary contributor
Mentor engineers around you
Partner closely with the Training Infrastructure engineer
How You'll Work.
Team & Collaboration
Lead by influence across MLOps, Autonomy, Data Platform, and TeleOp; Partner closely with the Training Infrastructure engineer
Process & Methodology
leading technical projects end-to-end
Full Job Description
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 embodied 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 the complex challenges like safety, commercialization, and mass production to change the world for the better. JOB SUMMARY Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform — the system of record for datasets, experiments, model artifacts, and serving paths that connects teleoperation data collection on one side to deployed autonomy on Apollo on the other. In this role, you will set the architecture for the platform layer above the training cluster: dataset lifecycle, experiment tracking, model registry, evaluation harnesses, and the serving / packaging path that delivers trained policies to robots in the field. You will lead by influence across MLOps, Autonomy, Data Platform, and TeleOp — establishing the standards, contracts, and tooling that turn one-off research code into a repeatable, auditable pipeline from data to deployed model. This is a hands-on technical leadership role, not a management position; you will be a primary contributor while mentoring the engineers around you, and partnering closely with the Training Infrastructure engineer who owns the cluster layer beneath the platform. ESSENTIAL DUTIES AND RESPONSIBILITIES Platform Architecture 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 (robot
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