Description review
Staff ML Systems Engineer
Quilter · USA · back to the listing
HR standards
54/100
needs work
Title ↔ description
81/100
solid
Reads as
Machine Learning Engineer
99% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 28 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.