Software Engineer - Frontier SI Infrastructure

Job Description

We are looking for strong software engineers to help scale the next generation of large SI models for Autopilot, Optimus & Digital Optimus. This role sits at the intersection of distributed systems, machine learning, and performance engineering. You will work closely with ML practitioners and infrastructure engineers to improve training efficiency, accelerate experimentation, and enable larger and more capable models.

The ideal candidate is excited about both systems and machine learning. You should be comfortable debugging distributed training issues, analyzing model behavior, and using data to demonstrate how infrastructure improvements translate into better model quality.

You will help build and optimize large-scale training systems running on thousands of GPUs while developing the tools, metrics, and workflows needed to make model scaling faster and more predictable.

Job Responsibilities

• Optimize large-scale distributed training across thousands of GPUs
• Improve training throughput, utilization, reliability, and scalability
• Develop tooling to identify bottlenecks in compute, networking, memory, and data pipelines
• Design and implement performance optimizations across PyTorch, CUDA, communication libraries, and training frameworks
• Partner with researchers to evaluate how infrastructure changes impact model quality, convergence, and downstream metrics
• Analyze training runs and build dashboards that connect system performance to model outcomes
• Drive improvements in model scaling efficiency, including larger models, longer context lengths, and higher-quality datasets
• Debug complex issues across software, hardware, networking, and machine learning systems
• Build infrastructure that accelerates experimentation and shortens iteration cycles for researchers

Job Requirements

• Strong software engineering fundamentals in Python and C++
• Experience with distributed systems, high-performance computing, or large-scale infrastructure
• Understanding of machine learning fundamentals, including optimization, training dynamics, and evaluation
• Familiarity with PyTorch and modern deep learning frameworks
• Ability to analyze performance bottlenecks using profiling and observability tools
• Strong debugging and problem-solving skills
• Excellent communication and collaboration skills