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
Requirements
- 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
Nice to Have
- 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
Benefits
- 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
Work Arrangement
Remote (Worldwide)
About Quilter
At Quilter, we are helping electrical engineers save time and accomplish more by automating the tedious and time-consuming task of designing printed circuit boards (PCBs). Our small team is composed of experts in electrical engineering, electromagnetic simulation, ML/AI, and high-performance computing (HPC). We are inventing and leveraging novel techniques to solve the decades-old problem of automating circuit board design where today hundreds of billions of dollars are spent. We have raised $25 million in Series B funding from some of the very best and are charging full-speed toward our goal. No matter where we come from, we're united by a common vision for the future and a core set of values we think will get us there: 1. Focus on the mission 2. Build great things that help humans 3. Demonstrate grit 4. Never stop learning 5. Pursue excellence
The Role
The Placer is responsible for automated component placement on PCBs. This role spans the full lifecycle: research, prototyping, productionization, and maintenance. You'll work across optimization, machine learning, and geometric deep learning on a hard, real-world combinatorial problem. This is a fully distributed team. We expect high autonomy and high ownership.
