Bedrock Robotics
Machine Learning Engineer: Evaluation
About this role
Bedrock is seeking an experienced ML Engineer to design and maintain evaluation systems for autonomous construction machinery. You'll translate real-world performance data into actionable metrics and statistical insights that drive product decisions and accelerate adoption of autonomous systems in construction.
What you'll do
- Build evaluation pipelines measuring performance across simulation, hardware-in-the-loop, and field data from deployed machinery
- Develop and connect product goals to measurable performance metrics from logged sensor data
- Implement infrastructure and classifiers to self-annotate data for training and testing datasets
- Create statistical models to predict system performance and assess deployment readiness at new construction sites
- Design and execute statistical tests to measure performance differences between system iterations
- Streamline evaluation workflows to enable other teams to gain insights earlier in development
What they're looking for
- Python and data warehouse query languages
- Statistical analysis (hypothesis testing, classification, uncertainty quantification, bias determination)
- ML and robotics system performance analysis
- Cloud-based parallelized computing frameworks
- Large dataset handling and processing
- Simulation and field data analysis
- Metrics design and product analytics
- Data annotation and classification systems
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Bedrock Robotics
Bedrock Robotics develops autonomous construction machinery powered by AI and robotics technology. The company is hiring for roles spanning developer infrastructure, simulation systems, hardware engineering, field robotics application, and frontend engineering to support the development and deployment of autonomous excavators and heavy equipment.
View all jobs at Bedrock RoboticsLikely interview questions
- Walk us through a time you designed an evaluation system for an ML model in production. What metrics did you use, and how did you validate they were meaningful?
- Describe your experience analyzing performance deltas between different system iterations. What statistical methods did you use, and how did you handle uncertainty in your results?