Senior ML Infra Engineer for Autonomous Driving

Rivian Automotive; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Rivian is seeking a full-time Senior ML, AI Infra Engineer to architect and optimize the AI infrastructure for training very large foundation models and accelerating inference.

You will collaborate with software engineers, ML engineers and researchers to push state-of-art ML for autonomous driving, designing scalable training pipelines and robust deployment.

Applicants should have PhD, PyTorch expertise, CUDA/Triton experience, and proven distributed training capabilities.

Qualifications

• PhD in CS/CE/EE, or equivalent industry experience.
• Deep knowledge of PyTorch.
• Experience with CUDA or Triton language for writing custom ops.
• Knowledge of model training framework.
• In-depth knowledge of transformer architecture and ways to accelerate training/inference.
• Experience of performing large scale distributed training of models.
• A track record of profiling model and doing detective work to improve model training and inference speed.

Responsibilities

• Design, train, and deploy large deep learning models that can leverage the vast amount of labeled and unlabeled data from a fleet of million vehicles.
• Improve ecosystem for training infrastructure and deployment pipeline to accelerate model iteration and improve performance.
• Provide thorough, constructive code reviews that enforce ML engineering standards, security practices, and performance optimization

Skills

PyTorch

Transformer models

Distributed training

Model profiling

Code reviews

ML infra

Education

PhD in CS/CE/EE

Tools

CUDA

Triton

Model training framework

Job description

Rivian is seeking a full-time Senior ML, AI Infra Engineer to architect and optimize the AI infrastructure for training very large foundation models and accelerating inference.

You will collaborate with software engineers, ML engineers and researchers to push state-of-art ML for autonomous driving, designing scalable training pipelines and robust deployment.

Applicants should have PhD, PyTorch expertise, CUDA/Triton experience, and proven distributed training capabilities.