Senior/Staff ML Systems Engineer

Quilter•Los Angeles, CA

9d•Remote

About The Position

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.

Requirements

5+ 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
Experience driving design reviews and improving engineering processes within a team
Comfort operating with high autonomy in ambiguous problem spaces
Experience with GPU-accelerated workloads and orchestration
Strong communication and collaboration skills

Nice To Haves

7+ years of industry experience
Familiarity with ML workflows involving optimization, RL, or combinatorial problems
Experience building infrastructure for small, research-heavy teams

Responsibilities

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