Sr ML Infrastructure Engineer, E2E Autonomy

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr ML Infrastructure Engineer, E2E Autonomy based in United States.

This role sits at the cutting edge of machine learning infrastructure and robotics, focused on building the end-to-end systems that power autonomous machines operating in real-world environments. You will help design and scale the full ML lifecycle—from data ingestion and training pipelines to on-vehicle inference and reinforcement learning feedback loops. The position plays a critical role in enabling large-scale deployment of intelligent robotic systems across complex domains such as agriculture and heavy machinery. You will work closely with ML engineers, robotics specialists, and product teams to translate research and real-world data into production-grade systems. This is a highly cross-functional and hands-on engineering role where ambiguity is expected, iteration is fast, and impact is tangible in the field. Your work will directly shape the future of end-to-end autonomy and its real-world applications.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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