Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Applied Scientist
- Remote from
- USA
- Salary
- USD 180k–200k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 10 Dec 2026
- Listing views
- 35
- Application actions
- 2
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About this role.
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:
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 page and visit our Blog.
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Sample interview questions
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.
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.
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.
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.
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.
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