Machine Learning Engineer

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Company Overview

A rapidly growing technology company is bringing advanced artificial intelligence and autonomous systems into the physical world. The organization develops autonomous solutions for heavy construction equipment, with a focus on improving job-site safety, accelerating project schedules, and supporting critical infrastructure projects.

The organization combines expertise from experienced leaders in autonomous vehicles, technology, transportation, and infrastructure with world-class engineering talent. Its autonomous systems are being deployed in real-world construction environments, creating opportunities for engineers to solve challenging problems that cannot be fully addressed through simulation alone.

The organization is seeking a Machine Learning Engineer with a focus on imitation and reinforcement learning, particularly data-driven behavior policies and robust machine learning data infrastructure. This role will involve developing and scaling state-of-the-art learning architectures while building the data systems required to make machine learning models reliable, scalable, and reproducible in production.

Key Responsibilities

• Design, train, validate, and launch models for behavior cloning and reinforcement learning.
• Build and maintain data ingestion, labeling, and management pipelines to ensure high-quality training datasets.
• Develop metrics and evaluation frameworks to measure model performance in open-loop testing, simulation, and real-world environments.
• Collaborate with simulation, systems, and infrastructure teams to integrate machine learning models into real-world autonomous systems.
• Deploy and debug machine learning models in real-world environments.
• Address practical challenges involving latency, hardware limitations, system integration, and production reliability.
• Develop scalable data and model infrastructure that supports reproducible machine learning development and deployment.
• Contribute to the development of advanced behavior-learning approaches for autonomous robotic systems.

Required Qualifications

• 3+ years of practical experience applying machine learning and deep learning frameworks such as PyTorch, TensorFlow, or JAX to solve real-world problems.
• 3+ years of professional experience building, deploying, and maintaining machine learning models in production environments.
• Familiarity with recent research literature and methods related to learned behavior policies.
• Practical experience with behavior cloning and/or reinforcement learning.
• Strong understanding of machine learning model development, training, evaluation, and deployment.

Preferred Qualifications

• Experience with diffusion policies, Vision-Language-Action (VLA) models, or related technologies.
• Published research or technical work in conferences such as ICRA, IROS, CoRL, CVPR, ECCV, ICCV, ICML, or NeurIPS.
• Experience working with autonomous robotic systems or other physical-world machine learning applications.
• Experience developing machine learning data pipelines and infrastructure for large-scale model training.

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