About the job
Join Quilter as a Senior Machine Learning Engineer
At Quilter, we empower electrical engineers by transforming the intricate process of designing printed circuit boards (PCBs) through automation. Our dedicated team, comprised of specialists in electrical engineering, electromagnetic simulation, machine learning (ML), artificial intelligence (AI), and high-performance computing (HPC), is pioneering innovative solutions to a longstanding challenge that has seen substantial investment over the decades. Backed by $25 million in Series B funding, we are rapidly advancing towards our ambitious goals.
Our team shares a unified vision for the future, driven by core values that guide our journey:
- Mission-Driven Approach
- Creating Exceptional Solutions to Aid Humanity
- Perseverance and Resilience
- Continuous Learning and Improvement
- Pursuit of Excellence
We are on the lookout for a Senior Machine Learning Engineer to enhance our Placer Team, focusing on developing AI that automates the component placement in PCBs.
The Role
As a key member of our team, you will oversee the automated component placement process on PCBs, engaging in all phases from research and prototyping to production and maintenance. You will tackle complex real-world combinatorial challenges using optimization, machine learning, and geometric deep learning techniques.
Our team operates in a fully distributed environment, where we value autonomy and ownership.
Responsibilities
- Lead projects from initial research and development through to production-ready, maintainable systems.
- Develop and enhance GPU-accelerated code using PyTorch and CUDA C++.
- Explore a diverse modeling landscape, including reinforcement learning, graph neural networks, classical optimization, and generative modeling.
- Define objectives, model constraints, and troubleshoot numerical behaviors within the system.
- Collaborate with senior colleagues to shape technical direction and research strategies.
Qualifications
- 4+ years of professional experience in machine learning, optimization, or related domains.
- Solid foundation in machine learning principles and optimization techniques.
- Proficient in programming languages and tools relevant to ML and optimization.
- Experience with GPU programming and frameworks such as PyTorch.
- Ability to work independently and manage multiple projects concurrently.

