About the job
Join Quilter as a Senior/Staff Machine Learning Systems Engineer
At Quilter, we are transforming the landscape of electrical engineering by streamlining the complex process of designing printed circuit boards (PCBs). Our talented team consists of specialists in electrical engineering, electromagnetic simulation, machine learning/artificial intelligence (ML/AI), and high-performance computing (HPC). With $25 million in Series B funding, we are poised to revolutionize an industry that spends billions annually on circuit board design.
We strive to create an environment where everyone shares a unified vision and operates under core values that guide our success:
Commitment to our mission
Innovating to enhance human capabilities
Exhibiting resilience
Continuous learning
Pursuing excellence in all endeavors
We are looking for a Senior or Staff ML Systems Engineer to join our Placer Team, focusing on building the robust infrastructure that transitions our research from prototype to reliable production.
The Role
The Placer is tasked with automating component placement on PCBs. Your primary focus will be on developing the systems and infrastructure that support the entire ML lifecycle, including training pipelines, data generation, cleaning, experiment management, orchestration, serving, A/B testing, and CI/CD processes. As we scale, your expertise will be invaluable for systems design reviews and long-term architectural planning.
This position is fully remote, and we value high levels of autonomy and ownership within our team.
Responsibilities
- Design, develop, and maintain ML infrastructure encompassing training, evaluation, serving, and monitoring phases.
- Manage data pipelines, including generation, cleaning, validation, and versioning.
- Enhance experiment tracking, orchestration, and reproducibility tools.
- Establish and maintain CI/CD pipelines and A/B testing frameworks.
- Lead and formalize the design review processes within the team.
- Proactively identify architectural risks and guide the team towards sustainable system decisions.
