Machine Learning Engineer: Evaluation

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Company Overview

A technology company is advancing autonomous systems for the construction industry, bringing artificial intelligence and robotics into real-world job sites. Its engineering team includes experienced professionals from the autonomous vehicle, logistics, and technology sectors and focuses on deploying autonomous heavy construction equipment to improve job-site safety, accelerate project schedules, and address challenges in critical infrastructure.

The organization has raised significant funding and has achieved fully autonomous excavator deployments in construction. Its work combines machine learning, robotics, and real-world construction expertise to solve complex physical-world problems that cannot be fully addressed through simulation alone.

The organization is seeking a Machine Learning Engineer: Evaluation to help develop advanced evaluation systems for autonomous construction technology. This role will focus on translating the complexity of real-world construction environments into actionable, AI-native evaluations that accelerate adoption and improve the performance and reliability of autonomous systems.

The successful candidate will have hands-on experience building evaluation systems, designing and executing statistical tests, and analyzing performance differences between iterations of complex machine learning systems. Experience working with ML or robotics systems deployed in production and real-world environments will be particularly valuable.

Key Responsibilities

• Design and maintain evaluation systems and pipelines for measuring autonomous system performance across open-loop and closed-loop simulation, hardware-in-the-loop systems, and field data from autonomous machinery.
• Develop streamlined evaluation workflows that enable engineering teams to identify insights earlier in the development cycle.
• Develop metrics that connect product objectives with system behavior by translating real-world specifications into measurable indicators from logged data.
• Provide objective insights to support decision-making across parameter tuning, system development, and program planning.
• Design infrastructure and classifiers for automatically annotating data and creating datasets for diverse machine learning training and evaluation use cases.
• Leverage machine learning models to generate rich annotations across large datasets and accelerate model development and iteration.
• Model performance metrics and interpret results across multiple data sources, including raw sensor data and key leading indicators.
• Identify potential challenges presented by new construction environments and determine their impact on system performance.
• Support deployment-readiness decisions by evaluating system performance and identifying hidden risks in new operating environments.
• Collaborate with engineering and cross-functional teams to improve evaluation processes and accelerate the development of autonomous systems.

Required Qualifications

• Senior- or Staff-level engineering experience with 5+ years of professional experience in software engineering, data science, or research.
• 2+ years of professional experience analyzing modern machine learning or robotics system performance on real-world problems.
• Strong proficiency in Python.
• Experience with a data warehouse query language and development within parallelized, cloud-based infrastructure.
• Strong statistical analysis skills, including classification, model-fit and bias analysis, hypothesis testing, and uncertainty quantification.
• Experience working with large datasets.
• Demonstrated ability to analyze complex systems and translate data into actionable insights.
• Experience working with production machine learning, robotics, or autonomous systems is highly valuable.

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