Senior ML Engineer at Jobgether – United States

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior ML Engineer in the United States.

The Senior ML Engineer will be instrumental in advancing AI-driven automation for complex engineering tasks. This role involves end-to-end ownership of the machine learning lifecycle, from research and prototyping to production deployment and maintenance. You will tackle real-world combinatorial problems using cutting-edge techniques such as reinforcement learning, graph neural networks, and optimization methods. The position offers the opportunity to work on GPU-accelerated ML pipelines, contribute to technical strategy, and collaborate closely with a highly skilled, distributed team. High autonomy, ownership, and the ability to navigate ambiguous problem spaces are essential. The ideal candidate is passionate about building scalable AI solutions that directly impact operational efficiency and innovation in engineering automation.

• Lead end-to-end development of ML systems, including research, prototyping, productionization, and maintenance.
• Design and implement GPU-accelerated code using PyTorch and CUDA C++ for complex modeling tasks.
• Develop models across a range of paradigms including reinforcement learning, graph neural networks, classical optimization, and generative modeling.
• Define model objectives, constraints, and debug numerical behavior across the stack.
• Contribute to technical direction, research strategy, and collaborative problem-solving within the team.
• Maintain high-quality, production-ready ML pipelines while iterating on innovative solutions to challenging combinatorial problems.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 4+ years of industry experience in machine learning, optimization, or a related domain.
• Strong fundamentals in machine learning and combinatorial optimization.
• Hands-on experience with production-level PyTorch codebases.
• Ability to navigate ambiguous problem spaces with high autonomy and ownership.
• Excellent communication and collaboration skills to work effectively across distributed teams.
• Preferred qualifications: 5–7 years of experience, CUDA C++ proficiency, and expertise in reinforcement learning, geometric deep learning, graph neural networks, multi-objective or combinatorial optimization.

• Competitive salary and equity benefits.
• Comprehensive health, dental, and vision insurance coverage.
• Flexible and fully remote work environment with high autonomy.
• Unlimited paid time off and paid parental leave.
• Regular team events and offsites (~4 times per year).
• Opportunities to work on challenging, mission-driven projects at the intersection of ML, optimization, and engineering automation.