[All remote jobs](https://jobicy.com/jobs.md)Open role[![Quilter logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/138e7ef9-221.png)](https://jobicy.com/company/quilter.md)Remote opportunity at[Quilter](https://jobicy.com/company/quilter.md) # Senior ML Engineer 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)Share25 Aug 2026Published26Listing views2Application actions24 Sep 2026Apply before Opportunity details ## About this role. AI SummaryQuilter 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 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior-level applied ML systems role involving research uncertainty, production engineering, numerical debugging, GPU acceleration, and complex constrained optimization. Success requires strong independent judgment across several advanced technical domains rather than implementation within a narrowly defined problem space. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$190,000US market range$170k–$230k0$253k AI insightThe disclosed yearly base salary range is $180,000 to $200,000 USD, with a midpoint of $190,000. For a US-based Senior ML Engineer specializing in optimization, deep learning, and CUDA/PyTorch systems, an estimated market base-salary range is approximately $170,000 to $230,000 USD annually; equity and benefits may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Optimization](https://jobicy.com/jobs?search_keywords=Optimization.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[CUDA C++](https://jobicy.com/jobs?search_keywords=CUDA%20C%2B%2B.md)[GPU Acceleration](https://jobicy.com/jobs?search_keywords=GPU%20Acceleration.md)[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Graph Neural Networks](https://jobicy.com/jobs?search_keywords=Graph%20Neural%20Networks.md)[Geometric Deep Learning](https://jobicy.com/jobs?search_keywords=Geometric%20Deep%20Learning.md)[Combinatorial Optimization](https://jobicy.com/jobs?search_keywords=Combinatorial%20Optimization.md)[Production ML](https://jobicy.com/jobs?search_keywords=Production%20ML.md) Sample interview questionsDescribe a machine-learning system you took from an early research idea to production. What changed during the process?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. How would you formulate PCB component placement as an optimization or learning problem? 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. What is your approach to debugging unstable numerical behavior in GPU-accelerated PyTorch or CUDA code? 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. When would you choose reinforcement learning over black-box or classical optimization for this problem? 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. How do you work effectively in an ambiguous, high-autonomy research engineering environment? 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](http://quilter.ai/about) page and visit our [Blog](https://quilter.ai/blog). Show more [Apply now >](https://jobicy.com/jobs/151566-senior-ml-engineer.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. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. Keep exploring ## Related remote jobs. Matched by job category10 related opportunities[Software Engineering](https://jobicy.com/categories/engineering.md) [Browse all jobs](https://jobicy.com/jobs.md) * ![Ashby logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/63864ee6-221.png) Ashby Aug 25 ### [Engineering Manager – Americas](https://jobicy.com/jobs/151638-engineering-manager-americas.md) Hi 👋🏾, I’m Abhik, Ashby’s Co-Founder and VP of Engineering. 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