About this role.
Quilter is seeking a senior-to-staff-level ML systems engineer to productionize machine learning for automated PCB component placement. The role owns the end-to-end ML platform, including training and data pipelines, evaluation, serving, monitoring, experiment management, orchestration, A/B testing, and CI/CD. The engineer will lead design reviews, identify architectural risks, and establish scalable engineering practices for a research-heavy team. Success requires substantial production ML experience, strong software and systems-design fundamentals, GPU workload expertise, and comfort operating independently in a distributed environment. This is a high-impact infrastructure role supporting optimization-oriented ML and long-range platform architecture.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would explain the initial research constraints, then describe how I standardized data contracts, versioned datasets and models, automated training and evaluation, implemented deployment gates, and added monitoring. I would quantify improvements such as reproducibility, deployment frequency, training reliability, inference latency, or incident reduction.
I would define immutable dataset and code versions, tracked configurations, environment pinning, artifact storage, lineage metadata, and a consistent experiment interface. I would make the compliant path easy through templates and orchestration so researchers can iterate quickly while results remain comparable and recoverable.
I consider workload profiles, GPU utilization, scheduling and queueing policies, distributed-training needs, data locality, fault tolerance, cost controls, observability, and capacity planning. For serving, I also evaluate model loading, batching, latency objectives, autoscaling behavior, rollback procedures, and hardware compatibility.
I begin by clarifying requirements, assumptions, scale expectations, and failure modes, then assess tradeoffs around reliability, security, maintainability, cost, and delivery speed. I document risks with their impact and likelihood, propose practical mitigations, and facilitate alignment on a decision that can be revisited as evidence changes.
I would define the hypothesis, eligible traffic or workload segmentation, primary quality metrics, safety guardrails, sample-size requirements, and rollback criteria before launch. I would monitor both model performance and system health, analyze results for statistically and operationally meaningful effects, and promote only when the change meets pre-agreed thresholds.
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
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 page and visit our Blog.
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