Senior ML Engineer — PCB Placement & GPU AI

Quilter; United States; On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Job summary

Quilter is seeking a Senior ML Engineer to join the Placer Team to automate PCB component placement. You will own the full lifecycle from R&D to production, building GPU-accelerated solutions in PyTorch and CUDA C++.

You will explore RL, graph neural networks, and optimization to advance practical PCB design automation, with high autonomy in a distributed environment. Ideal candidates have 5+ years in ML/optimization, strong ML fundamentals, production PyTorch experience, and the ability to

Qualifications

• 5+ years of industry experience in ML, optimization, or a related field.
• Strong fundamentals in machine learning and optimization.
• Production PyTorch experience.
• Ability to work across research and production codebases.
• Comfort operating with high autonomy in ambiguous problem spaces.

Responsibilities

• Own problems end-to-end from exploratory R&D through production-hardened, maintainable systems.
• Develop and extend GPU-accelerated code in PyTorch and CUDA C++.
• Work across a broad modeling landscape including RL, graph neural networks, black-box/classical optimization, and generative modeling.
• Formulate objectives, model constraints, and debug numerical behavior in the stack.
• Contribute to technical direction and research strategy alongside senior teammates.

Skills

ML fundamentals

Optimization

PyTorch

CUDA C++

Research to production

Communication

Tools

PyTorch

CUDA

Job description

Quilter is seeking a Senior ML Engineer to join the Placer Team to automate PCB component placement. You will own the full lifecycle from R&D to production, building GPU-accelerated solutions in PyTorch and CUDA C++.

You will explore RL, graph neural networks, and optimization to advance practical PCB design automation, with high autonomy in a distributed environment. Ideal candidates have 5+ years in ML/optimization, strong ML fundamentals, production PyTorch experience, and the ability to