Machine Learning Engineer: Evaluation

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.

- 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.

- Connect product goals and system behavior - by bridging real-world specification to measurable indicators from logged data. Empower confident decision making from parameter tuning to program planning by slicing through the noise and delivering objective insights.

- Implement infrastructure and classifiers - to self-annotate data and allow creation of datasets for a variety of training and evaluation use cases. Leverage models to source rich annotations for massive datasets to accelerate model iteration.

- Model metrics and interpret results - from various sources ranging from raw sensor data to key leading indicators. Determine whether new construction sites pose hidden challenges and drive business decisions about deployment readiness.

- 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.

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.