Machine Learning Engineer

Role overview This role designs and optimizes machine learning pipelines capable of handling petabyte-scale datasets used to train physical AI systems. The work is research-adjacent — the engineer collaborates directly with research teams to build models that learn from real-world data, owning the data and training infrastructure that turns raw sensor and physical-world inputs into learning signal.

Responsibilities - Designing and optimizing ML pipelines that operate at petabyte scale. - Working directly with research teams on model design and iteration. - Building data infrastructure that supports training on real-world physical AI datasets. - Improving throughput, reliability, and cost-efficiency of large-scale data processing.

Requirements - Hands-on experience designing and operating ML pipelines. - Familiarity with the data needs of physical AI or real-world sensor-driven models. - Comfort collaborating with research scientists and translating research needs into infrastructure.

Nice to have - Experience with distributed training systems and high-throughput data processing frameworks. - Background working on robotics, simulation, or other physical-world AI applications.