Senior ML Systems Engineer

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:

1. Focus on the mission

2. Build great things that help humans

3. Demonstrate grit

4. Never stop learning

5. Pursue excellence

We’re looking for a Senior ML Systems 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.

• Build and maintain ML CI/CD systems for model validation (accuracy, latency, I/O) and continuous delivery

• Develop and operate high-performance inference servers for low-latency PCB layout generation

• Build distributed data generation and model training frameworks to support large-scale geometric datasets

• Create and maintain ML infrastructure for scaling training and inference across multi-node systems

• Build tooling for A/B testing, controlled rollouts, and distribution drift detection

• Enable fast, rigorous experimentation through reproducible workflows, automation, and evaluation tooling

• Design and implement end-to-end training and inference pipelines, defining how data is created, prepared, consumed, and how model outputs are used

• Work with the team on model architecture decisions and optimize for GPU utilization and training speed

• Implement and optimize SL, SSL, and RL algorithms for geometric and PCB layout problems

• Build automated re-training pipelines to address distribution drift in production

• Strong experience with ML pipeline orchestration (Kubeflow, MLflow, or similar)

• Expertise in ML production systems (model serving, versioning, monitoring, CI/CD for ML)

• Experience with distributed training (multi-GPU, multi-node) using PyTorch

• Familiarity with hardware acceleration (CUDA, TensorRT) and memory optimization techniques (gradient checkpointing, mixed precision)

• Background in cluster management and job scheduling systems

• Familiarity with cloud platforms (AWS, GCP, or Azure) for compute, storage, and ML services

• Strong communication and collaboration skills

• Kubernetes experience (production deployments, scaling, monitoring)

• Infrastructure as code (Terraform, Helm)

• Container optimization for ML workloads

• Experience with model architectures for geometric data (transformers, CNNs, graph networks)

• Profiling and debugging tools for ML workloads (NVIDIA Nsight, PyTorch profiler, Weights & Biases)

• Experience with Reinforcement Learning, particularly combinatorial/constrained optimization problems

• Model compression techniques (knowledge distillation, pruning)

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.

• 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

Want to learn more about Quilter, our vision, and our investors? Visit our About page and visit our Blog.