About this role.
Quilter is hiring a Senior ML Engineer for its Placer Team to automate component placement for printed circuit boards. The role covers the complete technical lifecycle, from exploratory research and prototype development to production-grade maintenance. Core work combines machine learning, optimization, geometric deep learning, and GPU-accelerated systems development using PyTorch and CUDA C++. The engineer will address difficult combinatorial optimization problems and help shape research and technical direction. This is a distributed, high-ownership role primarily intended for US-based talent.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Sample interview questions
I would begin by defining measurable product and model objectives, then build a small reproducible prototype to validate feasibility. As the approach matures, I would add data and experiment versioning, automated tests, profiling, monitoring, robust failure handling, and clear interfaces. Production requirements often change the design by prioritizing latency, determinism, maintainability, and observability alongside model quality.
I would represent components, pins, nets, and board constraints as a graph or structured state. The objective would combine wirelength, routing feasibility, congestion, manufacturability, thermal or electromagnetic concerns, and hard geometric constraints, using penalties or constrained optimization where appropriate. I would evaluate classical solvers, learned heuristics, graph models, and reinforcement learning against reliable baselines on representative board families.
I first reduce the issue to a deterministic minimal case and validate inputs, tensor shapes, dtypes, device placement, and finite values at each critical operation. I compare custom kernels with a correct CPU or native PyTorch reference, use gradient checks where applicable, and profile for memory or synchronization problems. I then add targeted assertions and regression tests before optimizing the corrected implementation.
I would use classical or black-box optimization when objective evaluations are reliable, constraints are well specified, and strong solvers can efficiently search the space. Reinforcement learning becomes more attractive when placement is sequential, decisions benefit from learned priors across many instances, or exact evaluation is costly and amortized policy learning can improve throughput. In practice, I would benchmark hybrid methods and retain the simplest approach that meets solution-quality and runtime targets.
I translate ambiguity into explicit assumptions, measurable milestones, risks, and decision points, then share a concise plan with stakeholders. I deliver early evidence through baselines and prototypes, communicate results and tradeoffs clearly, and revise direction based on data. I also document decisions and build maintainable interfaces so the team can extend successful research without depending on a single engineer.
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:
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.
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 Youʼll 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 Weʼre 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.
Upload your resume now
- To unlock remote work opportunities and be discovered by global employers.
This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.
