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# Opportunistic Application

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/quilter.md)Share22 Aug 2026Published35Listing views4Application actions21 Sep 2026Apply before  Opportunity details

## About this role.

AI SummaryThis is an open, opportunistic application for candidates interested in Quilter’s mission to automate printed circuit board design. The company is especially seeking deep experience in reinforcement learning, geometric computation, or PCB design and layout. Work appears highly technical and cross-disciplinary, combining electrical engineering, ML/AI, electromagnetic simulation, and high-performance computing. Candidates should be motivated by ambiguous, mission-driven problem solving rather than a narrowly defined job scope. Hiring is primarily remote within the United States, with rare exceptions for exceptional candidates.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThe role targets difficult, long-standing automation challenges in PCB design and highlights advanced domains such as reinforcement learning, geometric computation, simulation, and HPC. Because it is an open application rather than a defined vacancy, candidates must also independently articulate where they can create the most impact.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate$155,000US market range$120k–$210k0$231k

AI insightNo numeric salary range is disclosed; "Competitive salary and equity benefits" is not sufficient to extract actual compensation. The figures shown are estimated US annual base-salary market ranges for a technical engineering, ML/AI, computational geometry, or PCB-automation candidate, with substantial variation based on specialty and seniority.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Geometric Computation](https://jobicy.com/jobs?search_keywords=Geometric%20Computation.md)[PCB Design](https://jobicy.com/jobs?search_keywords=PCB%20Design.md)[PCB Layout](https://jobicy.com/jobs?search_keywords=PCB%20Layout.md)[Electrical Engineering](https://jobicy.com/jobs?search_keywords=Electrical%20Engineering.md)[Electromagnetic Simulation](https://jobicy.com/jobs?search_keywords=Electromagnetic%20Simulation.md)[High-Performance Computing](https://jobicy.com/jobs?search_keywords=High-Performance%20Computing.md)[Algorithm Development](https://jobicy.com/jobs?search_keywords=Algorithm%20Development.md)[Technical Problem Solving](https://jobicy.com/jobs?search_keywords=Technical%20Problem%20Solving.md)

Sample interview questionsWhich of Quilter’s evergreen domains—reinforcement learning, geometric computation, or PCB design—best matches your experience, and what is a relevant project you have delivered?My strongest alignment is with the domain where I have delivered production-relevant results. I would describe the problem, constraints, technical approach, measurable outcome, and how the work could transfer to PCB-design automation.

How would you approach an ambiguous engineering problem with limited precedent or a poorly defined solution path?

I would begin by defining the user and technical success criteria, collecting representative data or examples, and decomposing the problem into testable hypotheses. I would build a small prototype quickly, evaluate it with agreed metrics, and iterate with domain experts before investing in a larger implementation.

Describe a time you worked across software, ML, and a specialized engineering domain. How did you ensure the solution was technically valid and useful?

I established a shared vocabulary and success metrics with domain experts, validated assumptions early, and used reproducible experiments to compare approaches. Regular reviews ensured that the implementation remained aligned with real engineering constraints rather than optimizing only abstract model metrics.

What evaluation framework would you use for an algorithm that automates part of PCB layout or design?

I would evaluate correctness and manufacturability first, including rule compliance and electrical constraints. I would then measure design quality, runtime, robustness across board types, and the amount of human intervention saved, while comparing results against expert baselines where available.

How do you balance research exploration with the need to ship reliable product capabilities?

I separate exploratory work from production commitments and define explicit decision gates for experiments. Once an approach demonstrates value, I focus on reliability, observability, performance, documentation, and integration with user workflows so the research outcome becomes a dependable product capability.

### 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:

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Focus on the mission

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Build great things that help humans

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Demonstrate grit

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Never stop learning

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Pursue excellence

### Job Description

Are you interested in working at Quilter but don’t see a job that matches your skillsets or qualifications? You’re in the right place.

We invite open applications from talented and enthusiastic individuals who feel personally connected to our mission. If you don’t find a current opening that matches your skills but believe you have something unique to offer, we’d love to hear from you.

While we gladly accept applications from everyone, we’re especially interested in people who have deep experience in any of the following “evergreen” Quilter domains:

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Reinforcement learning

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Geometric computation

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PCB design and layout

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:

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Interesting and challenging work

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Competitive salary and equity benefits

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Health, dental, and vision insurance

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Regular team events and offsites (~4x / year)

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Unlimited paid time off

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Paid parental leave

Want to learn more about Quilter, our vision, and our investors? Visit our [About](http://quilter.ai/about) page and visit our [Blog](https://quilter.ai/blog).

Show more

[Apply now >](https://jobicy.com/jobs/151364-opportunistic-application.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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