Staff Technical Program Manager, Autonomy

Rivian•Palo Alto, CA

About The Position

The Autonomy TPM team plays a key role in driving the most important initiatives within the Autonomy organization at Rivian. If you are looking for a team that just takes notes or is just a scrum master, this is not it. We are here to execute cross-functional projects that a) require tight coordination and dependencies, involving everything from sense and compute HW, Perception & Pose (heavily reliant on ML), Planning & Controls, Infrastructure & Simulation (critical for ML model training/validation), Systems Definition, Integration & Triage, etc., and b) need to be delivered in order to build a great Autonomy product.

Requirements

Bachelor’s Degree in Computer Science, Data Science, Engineering, or equivalent experience, with a strong foundation in areas relevant to AI/ML.
Experience in Technical Program Management on deep tech products or equivalent, specifically managing programs involving AI/ML components and data pipelines.
Hands on experience with ML-based products including evaluation with metrics driven analysis.
Strong interpersonal skills and ability to effectively communicate to external partner organizations to drive shared outcomes for Rivian, especially when collaborating on data sharing, model deployment, or platform integration related to AI/ML.

Nice To Haves

Experience with Autonomy systems, Robotaxis, and/or Robotics, particularly involving the integration and management of complex AI/ML models.
ML development in resource-constrained environments, demonstrating an understanding of the practical challenges of deploying ML on automotive-grade hardware.
You enjoy prototyping random side projects in your spare time, ideally related to AI/ML or robotics.

Responsibilities

Lead programs consisting of cutting edge technology projects, particularly ones that intersect with hardware, software, and regulatory constraints.
Exercise strong ownership over programs they lead, including diving into the technical details to drive smooth execution and briefing the VP of Autonomy & AI on progress and risks.
Drive pragmatic decisions and tradeoffs to maximize probability of hitting critical timelines, considering factors unique to ML projects such as model iteration speed, data annotation pipelines, and compute resource allocation.
Manage ambiguity by presenting options with a clear point of view for leadership, particularly concerning feature readiness, evaluation metrics, and performance trade-offs.
Understand the role of TPM as an insider/outsider, someone who drives accountability through influence and soft power rather than hard power and authority.
Mentor other TPMs on the team and help them grow their craft as TPMs, sharing expertise in managing ML-centric technical programs.