Machine Learning Engineer

• Own ML Problem Formulation: You will turn ambiguous picking performance goals into well-posed ML problems, deciding what the model predicts, how labels and rewards are defined, what loss to optimize, and what “better” actually means. This is the core of the role.
• 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 the 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 help 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.

• 5+ Years of Experience in Machine Learning and Computer Vision, including models you personally carried from problem statement to measured outcome.
• 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.
• Professional experience with at least one of the following:
• 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.

Pickle provides best-in-class benefits, including health, dental, and vision insurance; unlimited vacation; paid federal and state holidays; 401(k) contributions of 5% of your salary; company-covered travel expenses; and other perks to make your working life more fun, comfortable, and productive. FULL_TIME Organization