Sr ML Infrastructure Engineer, E2E Autonomy

Accountabilities:

• Design, build, and own end-to-end ML infrastructure, including data ingestion, training pipelines, inference systems, and reinforcement learning feedback loops.
• Scale data and training systems from early-stage datasets to large-scale production pipelines spanning thousands of hours of real-world data.
• Develop robust, extensible software architectures that operate across multiple robotic and vehicle platforms.
• Evaluate and integrate new machine learning models and architectures to improve system performance and autonomy capabilities.
• Collaborate closely with ML engineers and robotics engineers to solve complex cross-disciplinary problems and optimize system performance.
• Contribute to the definition of end-to-end autonomy strategy, ensuring alignment between data, models, and real-world deployment needs.
• Improve system reliability and performance across both on-vehicle and off-vehicle compute environments.
• Act as a technical bridge across teams, helping drive clarity in ambiguous problem spaces and enabling faster execution.

Requirements:

• 4+ years of professional experience in machine learning infrastructure, data platforms, robotics software, or related technical domains.
• Strong Python programming skills with experience building production-grade systems.
• Proven experience designing and delivering complex ML or data pipelines in production environments.
• Strong understanding of machine learning system architecture and end-to-end ML workflows.
• Ability to work effectively across multiple engineering disciplines, including ML, robotics, and platform engineering.
• Comfortable operating in ambiguous environments with evolving requirements and minimal predefined structure.
• Experience with large-scale data systems and model training infrastructure.
• Strong problem-solving, communication, and collaboration skills.
• Experience with robotics frameworks such as ROS is a plus.
• Familiarity with GPU programming or CUDA-based optimization is a strong advantage.
• Prior exposure to autonomous systems or heavy machinery domains is a plus.

Benefits:

• Competitive U.S.-based salary range of $160,000 – $287,000 plus bonus eligibility.
• Comprehensive health, dental, and vision insurance coverage.
• Bonus and performance-based incentive programs.
• Visa sponsorship available for eligible candidates.
• Flexible work arrangement with remote options and occasional travel to field and team sites.
• Opportunity to work on cutting-edge robotics and end-to-end autonomy systems.
• High-impact role with real-world deployment in agriculture, construction, and industrial environments.
• Inclusive and mission-driven culture focused on innovation, collaboration, and real-world impact.