Staff Machine Learning Engineer- Large Driving Model (LDM), Motion Planning, Autonomy

Rivian is building next-generation autonomy capabilities, including a Large Driving Model (LDM) that will power the company’s Autonomy stack. In this onsite Staff role in Palo Alto, you’ll help move advanced driving intelligence from research into production-grade, on-road performance, while guiding technical decisions across the autonomy organization.

What You’ll Do

• Own the full LDM lifecycle for targeted advanced driving capabilities, including data curation, post-training alignment, and rigorous evaluation pipelines.
• Analyze and improve model design choices end to end, with a focus on high-quality supervision, robust validation, and scalable architectures that narrow the gap between offline training and on-road behavior.
• Serve as a technical authority in Autonomy, providing definitive guidance on technology decisions, model architectures, and data strategies for current and future work.
• Evaluate technology tradeoffs with awareness of business, safety, and operational implications, balancing them against company timelines and product needs.
• Build scalable, extendable, collaborative ML systems and codebases, and establish best practices for the broader LDM team.
• Influence technical roadmaps and drive consensus across perception, planning, and platform teams, including during complex discussions.
• Lead the transition from development to deployment by deploying, supervising, and improving real-world on-road performance of your models in customer vehicles.

What You Bring

• M.S. or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field.
• 7+ years of industry experience delivering complex, production-grade Machine Learning systems, with a demonstrated track record at Staff or Lead level.
• Expert-level hands-on experience with Python and PyTorch.
• Experience with large-scale distributed training frameworks is highly preferred.
• Deep expertise in state-of-the-art foundation models, including post-training methods such as fine-tuning and alignment, plus experience applying these ideas to perception, prediction, or planning for autonomous driving or robotics.
• Proven ownership of ML data verticals, including data curation, synthetic data generation, and building robust evaluation frameworks for complex behavioral models.
• Strong fundamentals in linear algebra, geometry, optimization, algorithms, and data structures.
• Exceptional communication skills, including the ability to set technical direction, guide execution, and build consensus in a fast-paced environment.
• Motivation to take ambiguous, complex ideas from development and validation into a verified, shipping product.
• Hands-on approach: proactively finds systemic gaps and writes critical-path code when needed.
• Prior experience with Automotive Self-Driving stacks or similarly complex, safety-critical robotics systems.

Technologies

• Python
• PyTorch