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About This Role
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.
- 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.
- 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.
Preferred Qualifications
- 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Rivian, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Rivian AI Hiring
Rivian has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer, Research Engineer. Positions span Atlanta, GA, US, Palo Alto, CA, US, Riverdale, GA.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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