Summary
What you’ll impact
Machine Learning Engineer – New York, NY Our organization is seeking a Machine Learning Engineer focused on evaluation to design and maintain evaluation systems, develop metrics, classify data sources, and predict system performance for autonomous construction equipment. The role involves building pipelines, creating measurable indicators, implementing self-annotation infrastructure, and modeling performance to inform deployment decisions. This position is based in New York, NY.
Responsibilities
What you'll do
- Design and maintain eval systems: 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.
- Develop Metrics: 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.
- Classify Data Sources For Training And Testing: 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.
- Predict System Performance: 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.
Requirements
What you’ll bring
- 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.