About the Role

This role involves building and optimizing distributed training infrastructure for large-scale machine learning models, particularly deep learning and transformer architectures. The engineer will collaborate with scientists and engineers to deliver scalable, high-performance systems for state-of-the-art AI research and applications in robotics.

Key Responsibilities

• Design, build, and optimize machine learning infrastructure for large-scale training and inference
• Apply PyTorch, Python, and C++ skills to engineer modular, scalable ML systems
• Evaluate and implement parallelism techniques such as data, tensor, model, and pipeline parallelism
• Monitor and optimize GPU memory and throughput for training large models efficiently
• Collaborate cross-functionally with research and data infra teams to integrate new models and features

Required Skills & Qualifications

Must Have:

• 3+ years of non-internship professional software development experience
• 2+ years of non-internship design or architecture experience for new and existing systems
• Experience programming with at least one software programming language
• Deep understanding of LLM algorithms and deep learning frameworks like PyTorch
• Strong understanding of linear algebra, calculus, probability, and statistics

Nice to Have:

• 3+ years of full software development life cycle experience including coding standards, code reviews, source control, build processes, testing, and operations
• Bachelor's degree in computer science or equivalent

Benefits & Perks

• Full range of medical, financial, and/or other benefits
• Equity, sign-on payments, and other forms of compensation may be provided
• Inclusive culture with workplace accommodations for disabilities