Staff ML Systems 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 or Staff ML Systems Engineer to join Quilter's Placer Team and build the infrastructure that moves research from prototype to production reliably and efficiently.

The Role The Placer is responsible for automated component placement on PCBs. This role focuses on the systems and infrastructure that support the full ML lifecycle: training pipelines, data generation and cleaning, experiment management, orchestration, serving, A/B testing, and CI/CD. You'll be building the infrastructure that moves research from prototype to production reliably and efficiently. You'll also play a key role in systems design review and long-range architectural planning as the team scales.

This is a fully distributed team. We expect high autonomy and high ownership. What Youʼll Do Design, build, and maintain ML infrastructure across training, evaluation, serving, and monitoring Own data pipelines including generation, cleaning, validation, and versioning Build and improve experiment tracking, orchestration, and reproducibility tooling Implement and maintain CI/CD pipelines and A/B testing infrastructure Lead and formalize design review processes across the team Identify architectural risks early and guide the team toward sustainable systems decisions What Weʼre Looking For 7+ years of industry experience building and operating ML systems in production Proven track record as a key player in the success of a production-grade ML system end-to-end Deep familiarity with training pipelines, serving infrastructure, and experiment management Strong software engineering fundamentals and systems design sensibility Experienc