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# Machine Learning Engineer

- **Company:** Pickle Robot Company
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computer Vision, Code Review, Information Engineering, Python (Programming Language), Machine Learning, Pytorch, Docker, Unsupervised Learning
- **Published:** October 14, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-perception-pickle-robot-company-10439591

## About the Role

* 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.

## Description

* 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.