[All remote jobs](https://jobicy.com/jobs.md)[![Quilter logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/138e7ef9-221.png)](https://jobicy.com/company/quilter.md)[Quilter](https://jobicy.com/company/quilter.md) # Applied Scientist AI Summary Quilter is hiring a senior-to-staff Applied Scientist to advance automated printed circuit board design through research and high-performance software engineering. The role develops and validates algorithms spanning optimization, computational geometry, machine learning, simulation, and CPU/GPU performance. The successful candidate will contribute to architecture and technical roadmap decisions while mentoring engineers and improving team engineering standards. It requires substantial experience building large, performant production codebases, especially with Python and systems-oriented languages such as C++. The position is remote within the United States and targets experienced technical leaders with strong applied mathematics backgrounds.[Apply for this job](#job-application)[View company](https://jobicy.com/company/quilter.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 180k–200k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished10 Nov 2026Apply before10 Dec 2026Listing views41Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. ### About [Quilter](https://jobicy.com/company/quilter.md) 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: * Curiosity * Conviction * Rigor Quilter is looking for Senior to Staff Applied Scientists and Software Engineers to help invent the next generation of automated PCB design. You’ll design, implement and test state-of-the-art algorithms for some of the hardest problems in computational geometry, optimization, high-performance computing and physics simulation. As a senior member of the team, you’ll help shape the technical roadmap, mentor your peers and build the foundation for superhuman PCB design. You’ll work on one or more parts of the system, such as core learning algorithms, differential geometry techniques, state-of-the-art optimization methods, CPU and GPU backend performance, and fast physics simulation. ### What You’ll Do * Drive applied research toward the next breakthrough in PCB design. * Architect, implement and test high-performance libraries in Python, C++, Mojo, TypeScript, etc. * Lead by example: mentor engineers, give technical reviews and help raise the team’s engineering practices. ### What We’re Looking For * 3 to 10+ years of industry experience maintaining and extending large, high-performance codebases in collaborative environments. * A strong academic background in applied mathematics, with deep expertise in one or more of: optimization, machine learning, computational geometry, graph theory, high-performance computing (HPC), meshing, numerical methods, physics simulation, or a related field. * Proficiency in Python for prototyping and for integrating with ML systems. * Strong leadership skills: ownership, hiring and mentoring, sound decision-making and clear communication. ### Nice to Have * An advanced degree (M.Sc. or Ph.D.) in computer science, computational physics, robotics or a related field. * Expertise in GPU optimization and parallel or distributed algorithms. * Experience with reinforcement learning, CAD/EDA tools or physics-based optimization. * Contributions to open-source geometry, simulation or HPC projects. * Knowledge of physics engines, finite element methods (FEM) or distributed computing. 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](http://quilter.ai/about) page and visit our [Blog](https://quilter.ai/blog). Show more [Apply now >](https://jobicy.com/jobs/161034-applied-scientist-2.md) * ![Upload CV](data:image/svg+xml;base64,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) ### 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. ## 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 insightThis is a highly technical senior role combining advanced applied mathematics, performance engineering, physics simulation, and production software development. Candidates must independently solve ambiguous research problems while providing technical leadership and mentorship. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$170k–$230k0$253.00k AI insightThe employer explicitly provides a yearly base-pay range of $180,000–$200,000 USD, with a midpoint of $190,000. Estimated US market pay for senior/staff applied scientists working in optimization, HPC, and machine learning is approximately $170,000–$230,000 USD per year; equity may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied mathematics](https://jobicy.com/jobs?search_keywords=Applied%20mathematics.md)[Optimization algorithms](https://jobicy.com/jobs?search_keywords=Optimization%20algorithms.md)[Computational geometry](https://jobicy.com/jobs?search_keywords=Computational%20geometry.md)[Machine learning](https://jobicy.com/jobs?search_keywords=Machine%20learning.md)[High-performance computing](https://jobicy.com/jobs?search_keywords=High-performance%20computing.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[C++](https://jobicy.com/jobs?search_keywords=C%2B%2B.md)[GPU optimization](https://jobicy.com/jobs?search_keywords=GPU%20optimization.md)[Parallel computing](https://jobicy.com/jobs?search_keywords=Parallel%20computing.md)[Physics simulation](https://jobicy.com/jobs?search_keywords=Physics%20simulation.md)[Numerical methods](https://jobicy.com/jobs?search_keywords=Numerical%20methods.md)[Graph theory](https://jobicy.com/jobs?search_keywords=Graph%20theory.md)[Software architecture](https://jobicy.com/jobs?search_keywords=Software%20architecture.md)[Technical leadership](https://jobicy.com/jobs?search_keywords=Technical%20leadership.md)[Mentoring](https://jobicy.com/jobs?search_keywords=Mentoring.md) Sample interview questionsDescribe a technically difficult optimization problem you moved from research to production.I would explain the mathematical formulation, constraints, prototype approach, and validation metrics, then describe how I improved robustness and performance for production. I would also quantify the resulting gains in solution quality, runtime, or system reliability. How would you design a high-performance computational geometry library that supports research iteration and production reliability? I would define stable core abstractions, establish numerical-tolerance and correctness policies, and separate performance-critical kernels from higher-level Python interfaces. I would use property-based tests, benchmark suites, profiling, and a clear API versioning strategy to support both experimentation and dependable use. What is your approach to optimizing a CPU or GPU algorithm that has become a system bottleneck? I first profile representative workloads to identify whether computation, memory access, synchronization, or data movement is limiting throughput. I then test targeted changes such as parallel decomposition, memory-layout improvements, batching, vectorization, kernel fusion, or algorithmic reformulation and validate both speed and numerical correctness. How do you make sound technical decisions when research evidence is incomplete? I make assumptions explicit, identify the highest-risk unknowns, and run the smallest experiments that can invalidate a direction. I communicate trade-offs clearly, document decisions, and revisit them as new performance or quality data becomes available. How have you mentored engineers working on advanced technical systems? I combine regular technical reviews with collaborative problem framing and feedback tailored to each engineer's goals. I encourage clear experiment design, reproducible benchmarks, and ownership of decisions so that mentoring improves both individual capability and team standards. Next step ## Apply now. 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