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About This Role
An individual contributor role and the front door to Enterprise Productivity & AI. You bring in demand from across Rivian, translate it into a clear problem worth solving, and shape where our roductivity, collaboration, and AI investments go next.
Success depends on deeply understanding the business — how engineering, manufacturing, and corporate teams actually work — so recommendations land as real solutions rather than more tooling. You set direction and define requirements; platform administration, licensing, and renewals sit with other teams.
Focus areas: Atlassian (Jira, JSM, Confluence, Rovo, Marketplace plug-ins), AI frameworks and productivity agents (Claude Cowork, Claude Code, AWS Bedrock, Gemini, NotebookLM, Glean, Devin, Windsurf), and collaboration platforms (Slack, Google Workspace, Figma, Adobe, and others). Front door* Primary contact for business teams bringing productivity and tooling needs to the department; own intake, triage, and routing.
- Build relationships across functions and firsthand fluency in their workflows and constraints.
- Reframe requests into defined problem statements with clear outcomes; say no or not yet when warranted.
Strategy* Shape the portfolio roadmap — where to invest, consolidate, or sunset — backed by usage data, business impact, and total cost.
- Lead build-vs-buy-vs-configure decisions and document the trade-offs.
- Track the AI and workplace tooling landscape; advise leadership on what matters and what is noise.
Atlassian product ownership for engineering* Align Rivian’s engineering goals with the Atlassian platform.
- Standardize apps to optimize spend, reduce risk, and eliminate redundancy.
- Govern Confluence structure, templates, and knowledge access.
- Drive Atlassian Rovo adoption across engineering workflows.
- Lead integration and consolidation strategy with technical partners.
- Solve root delivery and process problems instead of over-customizing software.
AI and agents* Lead enterprise AI assistant and agent evaluation, rollout, and adoption strategy.
- Direct AI extension frameworks, including RAG, context management, and MCP integrations.
- Run hypothesis-driven pilots to measure time savings, output quality, and active usage.
- Partner with Security, Legal, and Privacy to establish guardrails and enablement programs.
- Align with engineering leaders to integrate coding agents into core developer workflows.
Delivery* Lead projects end-to-end using structured delivery frameworks to convert ambiguous needs into measured outcomes.
- Drive agile execution, maintaining responsive planning, backlog refinement, and review cadences.
- Partner with engineering teams to build custom tools, defining user stories, technical specs, and acceptance criteria.
- Manage end-to-end delivery mechanics, including timelines, dependencies, stakeholder alignment, UAT, and rollout.
- Bachelor's degree in IT, Business, Computer Science, or related field — or equivalent experience.
- 5+ years in product management, business analysis, digital workplace, or a related function.
- Track record of building stakeholder trust and translating messy needs into clear requirements.
- Deep hands-on Jira and Confluence experience in a working engineering environment + workflows, schemes, permissions, JQL, automation, and the Marketplace ecosystem + and able to hold a technical conversation with Atlassian admins and developers.
- Demonstrable daily use of AI assistants and productivity agents, with a point of view on where they help and where they fail.
- Working knowledge of agile practices and a structured methodology such as 5D Framework.
- Comfort operating independently across competing priorities.
- Strong communication skills.
Nice to have* Atlassian at scale — Cloud migration, plug-in rationalization, or portfolio planning (Jira Align or similar).
- Supporting engineering or technical organizations as a business partner.
- Hands-on Rovo Agents, Claude, or another enterprise search and knowledge-assistant platform.
- Coding agents and AI-assisted development environments in practice.
- AWS Bedrock or Google AI tooling in an enterprise context.
- Light scripting or low-code (Python, JavaScript, Jira automation) sufficient to prototype and talk to developers.
- Familiarity with IT security, data privacy, or compliance frameworks for workplace and AI tools.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Rivian, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Rivian AI Hiring
Rivian has 8 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer, AI Agent Developer. Positions span Atlanta, GA, US, Palo Alto, CA, US, Palo Alto, CA.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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