Job description

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: 1. Curiosity 2. Conviction 3. Rigor 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 - 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 PREFERRED - 7+ years of industry experience - Familiarity with ML workflows involving optimization, RL, or combinatorial problems - Experience building infrastructure for small, research-heavy teams 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 quilter.ai page and visit our Blog quilter.ai.

Requirements

Must-haves

This is a fully distributed team. We expect high autonomy and high ownership.
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
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
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
7+ years of industry experience

Nice-to-haves

Familiarity with ML workflows involving optimization, RL, or combinatorial problems
Experience building infrastructure for small, research-heavy teams
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaici/cd

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off

Parental Leave