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# Machine Learning Engineer

- **Company:** Bedrock Robotics
- **Location:** San Francisco, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Data Warehousing, Query Languages, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Software Engineering, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-evaluation-bedrock-robotics-inc-8126793

## About the Role

We're looking for a highly motivated engineer with experience evaluating complex ML systems deployed in the real world. Your Mission: Translate the infinite nuance of the built world into actionable, AI-native evaluations that accelerate Bedrock Operator adoption.

The ideal candidate has hands-on experience in building evaluation systems and designing and executing statistical tests to gauge performance deltas between system iterations. More importantly, you've iterated on complex ML systems run in production environments, and you understand the complexities that come with it., * Engineers who are currently Senior or Staff level with 5+ years of professional software engineering, data science, or research experience

 * 2+ years of professional experience analyzing modern ML or robotics system performance on real-world problems
 * Proficiency in Python and a data warehouse query language and comfort with development on infrastructure within parallelized cloud-based frameworks
 * Strong statistical analysis skills (e.g. classification, model fit bias determination, hypothesis testing, and uncertainty quantification)
 * Experience working with large datasets

***Bonus points: We're especially interested in engineers who have applied statistical backgrounds to ML research or real-world robotics applications.

## Description

* Build pipelines for measuring system performance - across open loop and closed loop simulation, hardware in the loop systems, and field data from Bedrock Operator equipped machinery. Excite other teams to gain insights earlier in the development cycle through streamlined workflows.


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