About Quilter
At Quilter, we are pioneering a revolutionary solution that automates the time-consuming and tedious task of designing Printed Circuit Boards (PCBs) by leveraging artificial intelligence. Our team of electrical engineers, electromagnetic simulation specialists, machine learning experts, and high-performance computing specialists are dedicated to inventing and implementing innovative techniques to address the decades-long challenge of automating PCB design. Since raising million in Series B funding, we are on a mission to revolutionize the PCB design industry. Our values of focusing on the mission, building great things that help humans, demonstrating grit, never stopping learning, and pursuing excellence align with the shared vision of our team.
The Role
The Placer role is a pivotal position on our distributed team, responsible for automating component placement on PCBs. This involves leading from end-to-end, from exploratory research through production-hardened, maintainable systems. The Placer will develop and extend GPU-accelerated code in PyTorch and CUDA C++ and work across a broad modeling landscape, including reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, and combinatorial optimization. The Placer will formulate objectives, model constraints, and debug numerical behavior in the stack and contribute to technical direction and research strategy alongside senior teammates. This role requires a 5+ year industry experience in machine learning, optimization, or a related field, strong fundamentals in machine learning and optimization, production PyTorch experience, demonstrated ability to work across research and production codebases, comfort operating with high autonomy in ambiguous problem spaces, and strong communication and collaboration skills.
What You'll Do
- 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 reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, and combinatorial optimization
- Formulate objectives, model constraints, and debug numerical behavior in the stack
What We're Looking For
- 5+ years of industry experience in ML, optimization, or a related field
- Strong fundamentals in machine learning and optimization
- Production PyTorch experience
- Demonstrated ability to work across research and production codebases
- Comfort operating with high autonomy in ambiguous problem spaces
- Strong communication and collaboration skills
Preferred
- 5–7 years of industry experience (Staff-level appointment may be considered)
- CUDA C++ experience
- Background in any combination of: reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, combinatorial optimization
Please note: We are an equal opportunity employer. At this time, we are focused on hiring primarily within the US, with occasional exception to accommodate exceptional talent.
What We Offer
- Interesting and challenging work
- Competitive salary and equity benefits
- Health, dental, and vision insurance
- Regular team events and offsites (~4x / year)
- Unlimited paid time off
- Paid parental leave
Want to learn more about Quilter, our vision, and our investors? Visit our About page and visit our Blog.
Note: We are an equal opportunity employer. At this time, we are focused on hiring primarily within the US, with occasional exception to accommodate exceptional talent.
Contact Us: For more information or to apply, visit our career page at https://jobicy.com/company/quilter/careers.
Are you a skilled and motivated Senior ML Engineer seeking to join a pioneering team that is revolutionizing the PCB design industry? Apply now to join the future of PCB design.
