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

Rivian Palo Alto, CA

Palo Alto, CA

Today

Full time Artificial Intelligence Autonomous Driving Data Pipeline

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Job Description

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

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