Tech Lead, ML Training & Inference for Autonomous Driving

Rivian Automotive; England; On-site; GBP 110,000 - 150,000; Full time

Job summary

Rivian is seeking a Tech Lead - Manager to join the Perception team in the United Kingdom, focusing on building state-of-the-art ML training and infrastructure for autonomous driving models. You will drive performance improvements and architectural decisions that enable scalable, secure, and maintainable systems.

You will optimize training performance on NVIDIA GPUs, reduce inference latency, and collaborate with cross-functional teams to accelerate model development and deployment.

Qualifications

• PhD in CS/CE/EE, or equivalent, with industry experience.
• Deep knowledge of PyTorch, TrensorFlow or Jax.
• In-depth knowledge of transformer architecture and ways to accelerate training/inference.
• Experience of performing large scale distributed training of models.
• Track record of profiling models and improving training/inference speed.

Responsibilities

• Optimize the performance of Deep Learning training workload on NVIDIA GPU systems at scale.
• Optimize the latency of model inference and preprocessing on onboard systems.
• Design, train, and deploy large deep learning models from diverse data.
• Drive architectural decisions, design reviews, and engineering standards for scalability, security, and maintainability.
• Identify technical dependencies and bottlenecks early to maintain momentum.
• Establish and enforce team guidelines for code reviews, testing, deployment, and operational excellence.

Skills

PyTorch

TrensorFlow

Jax

Education

PhD in CS/CE/EE or equivalent

Tools

model training framework

PyTorch

TrensorFlow

Jax

Job description

Rivian is seeking a Tech Lead - Manager to join the Perception team in the United Kingdom, focusing on building state-of-the-art ML training and infrastructure for autonomous driving models. You will drive performance improvements and architectural decisions that enable scalable, secure, and maintainable systems.

You will optimize training performance on NVIDIA GPUs, reduce inference latency, and collaborate with cross-functional teams to accelerate model development and deployment.