Senior Scaled ML 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 Scaled ML 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.

• Conduct modality and architecture searches: design custom models and evaluate OTS architectures and pretrained weights

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

• Develop scalable, distributed training workflows, including data creation pipelines and definitions of training behavior at large scale

• Design and maintain robust data generation systems, including dataset versioning, testing, and strategies for producing high-quality training data

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

• Improve system performance and GPU utilization, applying techniques to accelerate training and inference

• Ensure algorithmic scalability, solving real pain points that arise as data volume, model size, and cluster size increase

• Build automated re-training pipelines and work with Systems Engineers to detect and address distribution drift in production

• Experience with large-scale training of models with 100M+ parameters

• Expertise in distributed training using PyTorch across multi-GPU and multi-node environments
  Knowledge of memory optimization techniques such as gradient checkpointing, mixed precision, and parameter sharding

• Familiarity of training infrastructure including cluster management and job scheduling systems

• Background in model architecture design across transformers, CNNs, and graph networks for geometric data

• Experience with performance optimization focused on training speed, convergence, and scaling laws

• Experience with Reinforcement Learning - combinatorial/constrained optimization problems, sequential decision-making.

• Knowledge of Model Compression Techniques - knowledge distillation, pruning strategies, etc.

• Expertise with Attention Mechanisms - specifically spatial/geometric attention

• ML Tooling Experience - NVIDIA Nsight, PyTorch profiler, Weights & Biases

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.