Senior Distributed Systems Engineer

We are seeking highly skilled engineers with expertise in machine learning, distributed systems, and high-performance computing to join our Research team. In this role, you will collaborate closely with researchers to build and optimize platforms that train next-generation foundation models on massive GPU clusters. Your work will play a critical role in advancing the efficiency and scalability of cutting-edge generative AI technologies.

Key Responsibilities

• Scale and optimize systems for training large-scale models across multi-thousand GPU clusters.
• Profile and enhance the performance of training codebases to achieve best-in-class hardware efficiency.
• Develop systems to distribute workloads efficiently across massive GPU clusters.
• Design and implement robust solutions to enable model training in the presence of hardware failures.
• Build tools to diagnose issues, visualize processes, and evaluate datasets at scale.
• Optimize and deploy inference workloads for throughput and latency across the entire stack, including data processing, model inference, and parallel processing.
• Implement and improve high-performance CUDA, Triton, and PyTorch code to address efficiency bottlenecks in memory, speed, and utilization.
• Collaborate with researchers to ensure systems are designed with optimal efficiency from the ground up.
• Prototype cutting-edge applications using multimodal generative AI.

Qualifications

3 years of professional experience in ML pipelines, distributed systems, or high-performance computing.

Proficiency in Python, with significant experience using PyTorch.

Note : This position is not intended for recent graduates.

Compensation

The salary range for this role in California is $175,000-$250,000 per year. Actual compensation will depend on job-related knowledge, skills, experience, and candidate location. We also offer competitive equity packages in the form of stock options and a comprehensive benefits plan.

Salary : $175,000 - $250,000