Senior MLOps Engineer

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September 20

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Quilter

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Hardware • SaaS • Artificial Intelligence

Quilter is an innovative technology company offering an all-in-one PCB design tool tailored for hardware teams. This platform enables engineers to automate and optimize circuit board layouts, reducing design time from weeks to mere hours. Quilter's software is compatible with existing design tools like Altium and KiCAD, and it imports design constraints to optimize for EMI performance and manufacturing. Offering generative AI-driven design processes, Quilter provides numerous quality design candidates that are production-ready. The company's "Fab for Free" program and open beta version make it a cost-effective choice for electronics companies worldwide.

2 - 10 employees

🔧 Hardware

☁️ SaaS

🤖 Artificial Intelligence

📋 Description

• 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 • Join Quilter’s ML Team as an early engineer with ownership and influence over product, architecture, and team culture • Bridge software and hardware development to support automated PCB design

🎯 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 • 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) • Primarily hiring within the US (occasional exceptions for exceptional talent)

🏖️ 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