Senior ML Ops Engineer

Quilter • Los Angeles, CA

97d

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 $10 million in series-A funding from some of the very best and are charging full-speed toward our goal. We’re looking for a Senior ML Ops Engineer to join Quilter’s ML Team and help us build the software platform behind the future of circuit board design. We are a team of generalists who pride ourselves on solving new challenges and always learning. As one of our early engineers, you’ll have massive ownership and influence over the direction of our product, architecture, and team culture. This role is ideal for someone who thrives in high-ownership environments, loves solving complex technical problems, and is excited by the idea of bridging the worlds of software and hardware development.

Requirements

• Strong experience with ML pipeline orchestration (Kubeflow, MLflow, or similar platforms)
• Expertise in ML production systems (model serving, versioning, monitoring, CI/CD for ML)
• Experience with distributed training (multi-GPU, multi-node) and hardware acceleration (CUDA, TensorRT, or similar)
• Familiarity with cloud platforms (AWS, GCP, or Azure) for compute, storage, and ML services
• Strong communication and collaboration skills for working with cross-functional teams

Nice To Haves

• Kubernetes familiarity (production deployments, scaling, monitoring)
• Knowledge of infrastructure as code (Terraform, Helm, or similar)
• Experience with containerization (Docker, container optimization for ML workloads)
• Solid software engineering and DevOps background (containers, CI/CD pipelines, infrastructure automation)
• Background in monitoring and observability for ML systems (model performance tracking, drift detection)
• Cloud platform experience (AWS, GCP, or Azure ML services and compute)

Responsibilities

• Build and maintain ML training and inference infrastructure
• Implement automated model deployment and monitoring systems
• Optimize model serving for low-latency PCB layout generation
• Scale training infrastructure for large geometric datasets
• Ensure reliability and performance of production ML systems

Benefits

• Interesting and challenging work
• Competitive salary and equity benefits
• Health, dental, and vision insurance
• Regular team events and offsites (~2x / year)
• Unlimited paid time off
• Paid parental leave

What This Job Offers

• Career Level: Senior
• Number of Employees: 1-10 employees