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🌐 Remote💼 Full-time🗓 2026-06-25

Core

Automating component placement on PCBs using optimization, machine learning, and geometric deep learning.

Role type

Senior IC machine-learning engineer (optimization & geometric deep learning)

Builds

Production systems for automated PCB component placement

Domain

Hardware design / Electrical engineering / Combinatorial optimization

Deliverable

production ML models

Required skills

Machine learning fundamentals, Optimization, PyTorch, GPU programming, Reinforcement learning, Graph neural networks, Generative modeling, Numerical debugging

Preferred skills

CUDA C++, Multi-objective optimization, Combinatorial optimization, Staff-level experience

Technologies

PyTorch, CUDA C++, GPU

Responsibilities

Develop and extend GPU-accelerated code, Formulate objectives and model constraints, Debug numerical behavior, Contribute to technical direction and research strategy

Seniority

Senior, hands-on IC

Rewrite
## About 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. ## 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 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 ## 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 ## 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 ## About the company 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: - Focus on the mission - Build great things that help humans - Demonstrate grit - Never stop learning - Pursue excellence We're looking for a Senior ML Engineer to join Quilter's Placer Team and help us build the AI that automates component placement on PCBs.
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