Staff Software AI Engineer

Rivian•Palo Alto, CA

1d•Onsite

About The Position

Rivian’s Applied AI team builds AI products that help engineers across the company design, build and support our vehicles. As a Staff AI Software Engineer specializing in agentic applications, you will shape the technical direction for how Rivian integrates AI into the way its people work. You will bring large language models together with enterprise knowledge and tools to build internal applications at scale, beginning with core product engineering workflows and expanding across the vehicle development lifecycle. Your focus will be on automating complex workflows, accelerating decision-making and reducing manual effort. You will establish the architecture, engineering standards and evaluation practices needed to deliver reliable, scalable and observable AI applications, with measurable impact and capabilities that can be reused across the organization. You will partner with Rivian’s product, systems and data teams while remaining hands-on in the design and implementation of the most critical components. This role will be located in Palo Alto, CA and report to our Sr. Manager, Applied AI for Product Engineering.

Requirements

8+ years of professional software engineering experience, including technical leadership of complex production systems.
Shipped and operated LLM-based or agentic applications with real users in production, with evidence of measured quality, adoption and business impact.
Deep understanding of agent system design: orchestration, tool calling, retrieval and grounding, context and memory management, durable execution, verification and evaluation of nondeterministic systems.
Expertise in backend and distributed systems: APIs, asynchronous execution, state management, fault tolerance and observability at scale.
Expert-level Python and experience deploying services on cloud infrastructure with containers and automated delivery pipelines.
A record of setting technical direction and engineering practices adopted beyond your own team, with demonstrable impact on the systems and engineers that depended on them.
Experience integrating with enterprise systems and data under security, privacy and permission constraints.
Ability to turn ambiguous user needs into practical technical solutions and to communicate clearly with engineers, domain experts and executives.
Bachelor’s, Master’s or Ph.D. in Computer Science, Engineering or a related field, or equivalent practical experience.

Nice To Haves

Experience with multi-agent systems that include verification or adversarial checking, persistent state, and recovery or human-approval mechanisms.
Experience with multimodal applications, document or engineering-drawing understanding, vision models or computer-use agents.
Familiarity with product development tooling: CAD, PLM, simulation or CAE, or physics-informed machine learning.
Experience improving AI systems through failure analysis, feedback loops, model adaptation or fine-tuning.
Experience building AI infrastructure or capabilities adopted by multiple teams, or forward-deployed experience at an AI lab or agent-infrastructure company.

Responsibilities

Platform architecture. Define and own the agent platform architecture, including orchestration of long-running, multi-step agents, persistent state management, tool integration, permission-aware access to enterprise data, and evaluation infrastructure that gates production releases.
Technical direction. Establish reusable patterns, interfaces and engineering standards so that capabilities developed for one application can be adopted across the organization.
Production quality. Set the bar for reliability, observability and evaluation of AI systems. Ensure production applications meet defined quality, latency and cost targets against established baselines.
Hands-on engineering. Lead the design and implementation of the most complex components, deliver reference implementations, and resolve critical integration and reliability challenges.
Technology strategy. Evaluate emerging models, frameworks and tools; lead technical decisions on build versus buy and vendor selection; translate relevant advances into a roadmap with measurable impact.
Security and governance. Partner with Security, Legal and Compliance to embed authorization, auditability and data governance into the platform and its applications.
Talent development. Raise the engineering bar through design and code reviews, mentoring and participation in hiring.