Strategic Overview
Senior ML Engineer at Quilter is a aiml opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as 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++.
The role is listed for United States with remote working arrangement, giving candidates the practical context needed to assess fit before applying. Relevant signals in the listing include Software Engineering, Senior.
Key information at a glance
About This Role
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.
Key Responsibilities & Objectives
- 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
Required Qualifications & Candidate Blueprint
- 5+ years of industry experience in ML, optimization, or a related field
- Strong fundamentals in machine learning and optimization
- Production PyTorch experience
Preferred Qualifications
- 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
Salary & Benefits
Published compensation: USD 180,000 – 200,000 / yearly
- 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
Salary Benchmark
This role’s published compensation is compared with current employer-disclosed salary ranges for the same role at the Senior level in the same currency.
Benchmark uses active employer job postings in our database and excludes this listing. It is not a third-party salary estimate, and currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This aiml opening is a useful opportunity for candidates whose experience matches Software Engineering, Senior. Its strongest career signal is the scope of the work itself — particularly Own problems end-to-end from exploratory R&D through production-hardened, maintainable systems — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground Software Engineering, Senior, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.