Senior ML Engineer

Experience: 5+ years of industry experience in machine learning, optimization, or a related field Core Areas: Machine Learning, PyTorch, CUDA C++, Reinforcement Learning, Graph Neural Networks, Geometric Deep Learning, Generative Modeling, Combinatorial Optimization Compensation: $180,000 – $200,000 per year, plus equity About the Role This opportunity is for a Senior ML Engineer focused on developing AI systems for automated component placement on printed circuit boards. The role owns complex technical problems across the full lifecycle, from exploratory research and prototyping through productionization and long-term maintenance, with work spanning machine learning, optimization, and geometric deep learning. The position involves developing GPU-accelerated systems with PyTorch and CUDA C++, working with reinforcement learning, graph neural networks, generative modeling, and classical and black-box optimization. The role also contributes to model objectives and constraints, numerical debugging, technical direction, and research strategy while operating with substantial autonomy and ownership. What You'll Do Own complex technical problems end-to-end, taking projects from exploratory research and development through prototyping, productionization, and maintainable production systems. Develop and extend GPU-accelerated machine learning code using PyTorch and CUDA C++. Build and evaluate modeling approaches across reinforcement learning, graph neural networks, geometric deep learning, black-box and classical optimization, and generative modeling. Formulate optimization objectives and model constraints while investigating and debugging numerical behavior throughout the technical stack. Contribute to technical direction and machine learning research strategy alongside senior technical team members. Qualifications Required Experience 5+ years of industry experience in machine learning, optimization, or a related field. Production experience developing machine learning systems with PyTorch. Demonstrated experience working effectively across both research and production codebases. Required Skills Strong fundamentals in machine learning and optimization. Ability to work with substantial autonomy and ownership in ambiguous technical problem spaces. Strong communication and collaboration skills for working on complex research and engineering problems. Preferred Qualifications 5–7 years of relevant industry experience; a Staff-level appointment may be considered based on experience and scope. Hands-on experience developing GPU-accelerated software with CUDA C++. Experience with one or more of the following areas: reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, or combinatorial optimization. Health, dental, and vision insurance. Company equity. Unlimited paid time off. Paid parental leave. Regular team events and offsites, approximately four times per year. Opportunity to work on challenging technical problems across machine learning, optimization, and production AI systems. #J-18808-Ljbffr 7Seventy