Senior Machine Learning Engineer - Perception

Build the Data Engine: You will own dataset strategy end-to-end, from large-scale synthetic scene generation and distributed cloud dataset jobs to scene diversity filtering and self-supervised label generation. You will identify when data, rather than the model, is the bottleneck and fix it.

Train and Iterate on Large Vision Models: You will fine-tune large backbones with multiple task-specific heads, run disciplined experiments, and know both which experiment to run next and when to stop.

Own Evaluation and Verification: You will define offline metrics that predict on-robot outcomes and online metrics that let you improve performance on edge cases.

Drive Technical Excellence: As a senior member of the team, you will conduct rigorous code reviews, mentor engineers earlier in their careers, and contribute to the perception and grasping roadmap.

Hand Off Cleanly to Production: You will produce deployable models with sane latency budgets, exportable graphs, and reproducible artifacts, partnering with the engineers who own runtime integration and inference optimization rather than owning that yourself.

A track record of framing problems. You can translate high-level product requirements into specific engineering tasks and explain technical trade-offs to non-expert stakeholders.

Hands-on Experience Building Datasets at Scale: generation or collection, curation, automated quality assurance, coverage and bias analysis, and active learning to systematically close edge cases.

Rigorous Evaluation Habits: baselines, ablations, metric design, and a clear sense of when a result is real.

Expert-level Python and PyTorch skills.

Comfortable with cloud training infrastructure (GCP or AWS), Docker, and experiment tracking tools.

Developing and training multi-task models

Developing and training multi-modal models

Developing and training multi–view models

Self-supervised, outcome-based, or reward-driven learning.