Top AI & Machine Learning Jobs in Austin, TX

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Build production ML infrastructure for humanoid robotics, including multimodal data curation, annotation, versioning, large-scale pipelines, simulation and evaluation harnesses, model registries, deployment promotion, experiment tracking, and developer tooling. Partner with autonomy, data platform, and teleoperation teams on lifecycle contracts and technical direction while mentoring engineers through code and design reviews.

Lead development and deployment of state-of-the-art RL algorithms for locomotion and manipulation on humanoid robots. Prototype in simulation, transfer and fine-tune on hardware, optimize distributed training pipelines, mentor junior engineers, and collaborate with hardware teams to analyze results and improve sim-to-real performance.

As a Staff MLOps Engineer, you will lead the architecture of the MLOps platform, manage dataset lifecycle, model registry, and ensure quality and deployment of models to robots.

Lead the Human Robot Interaction product vision and roadmap, define and prioritize HRI features, collaborate with UX, engineering, and research to design intuitive multimodal interfaces, validate interactions before deployment, and align product work to customer value and commercialization goals.

Design and implement state-of-the-art RL algorithms and scalable training pipelines for whole-body locomotion and manipulation; transfer and fine-tune policies from simulation to physical humanoid robots; build motion retargeting from human demonstration; collaborate with hardware and controls teams to diagnose system-level issues and demonstrate progress on robot hardware.