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ABOUT THE TEAM
The People Operational Excellence \& AI team sits at the intersection of process design, people systems, and artificial intelligence. We build the operating infrastructure that enables Rivian's 14,000\+ person workforce — from the plant floor in Normal, IL to corporate and commercial teams — to move faster, smarter, and with fewer manual workarounds.
We own the technology portfolio, AI adoption strategy, and process transformation agenda for Rivian's People function. Our current priorities span a portfolio of AI\-enabled automation initiatives across the employee lifecycle — all in service of building a People team that operates with the same rigor and precision as the vehicles we make.
ABOUT THE ROLE
We're looking for someone who can map a broken process in the morning and design an AI\-assisted future state in the afternoon. This role is the connective tissue between how Rivian's People team works today and how it needs to work to meet the growing demands and complexity of our rapidly scaling global organization.
You'll lead process mapping and redesign across our highest\-priority initiatives — Talent Acquisition transformation, Internal Mobility, onboarding, employee support, and shared services — while bringing an AI efficiency lens to every intervention. You'll translate process realities from the ground floor into system requirements, identify where automation creates genuine leverage, and help the People team build the muscle to work differently.
This is a consulting, project management, and hands\-on builder role all at once. It's an operator who diagnoses like a consultant, drives like a project manager, designs like a systems thinker, and builds things that stick.
WHY THIS ROLE MATTERS
- Design the way People work: Help define how core People work is structured, documented, and executed across COEs, Shared Services, Talent Acquisition, and People Partners so Rivian can scale with less friction and more clarity.
- Unlock capacity through AI and automation: Identify practical opportunities to apply AI agents, workflow automation, and knowledge management to reduce manual work, improve quality, and create more space for strategic work.
- Connect process reality to platform decisions: Bridge People, IT, and Data to ensure our systems and workflows reflect how work actually happens on the ground — not just how it is intended to happen on paper.
- Strengthen the foundation for scale: As Rivian continues to grow, help standardize global practices, improve cross\-team handoffs, and build the People infrastructure needed to support a 14,000\+ person workforce across manufacturing, commercial, and corporate environments.
Process Mapping \& Redesign
- Map processes end\-to\-end, not just on paper. Lead current and future\-state process mapping across People initiatives — from Talent Acquisition and Internal Mobility to onboarding, employee support, and shared services. Go deep enough to surface the real problems: the workarounds, the undocumented exceptions, and the places where work falls through the cracks between teams.
- Establish practical standards. Define process design, documentation, handoff, and governance standards so teams can execute more consistently and scale with less variability.
- Identify and eliminate friction. Surface sources of avoidable demand and manual lift, then lead simplification efforts with measurable impact on cycle time, quality, throughput, and user experience.
AI, Automation \& Digital Enablement
- Design AI\-and\-Workday\-enabled process solutions. Translate process problems into concrete solutions that leverage Workday and AI agents. Prototype where it helps make the idea real, and partner with Workday and Engineering teams to get them built.
- Turn process maps into system requirements. Translate mapped workflows into clear, actionable requirements that Engineering \& Technology partners can build and configure in Workday, ServiceNow, iCIMS, and related tools. Be the bridge between what the business needs and what the technology team delivers.
- Build working solutions, not just recommendations. Use AI\-assisted development tools to move from a business problem to a working prototype alongside HR leaders — building custom scripts, lightweight automations, and workflow tools that solve real, everyday pain without waiting in an engineering queue. You don't need to be a software engineer; you need to be someone who can sit with a stakeholder, understand what's actually broken, and ship something that works.
- Find and scope automation opportunities. Identify where AI agents, workflow automation, case deflection, or LLM\-assisted tools can eliminate manual lift — and where they can't. Develop structured business cases for investment prioritization, including effort estimates, projected savings, and risk.
- Apply sound judgment. Distinguish where AI creates real leverage, where workflow automation is the better answer, and where process redesign or change management is the actual need.
Measurement \& Operational Rigor
- Measure what matters. Define baseline and target metrics — cycle time, cost, volume, error rate — for every process redesign, and build the dashboards needed to track adoption and savings after launch, not just at the design stage.
- Partner with IT, People Systems, and Data teams to ensure solutions are grounded in real workflows, integrated thoughtfully, and supported by strong measurement and feedback loops.
Facilitation \& Cross\-Functional Leadership
- Facilitate high\-stakes working sessions. Run process mapping workshops, future\-state design sessions, and cross\-functional working groups with HR leaders, HRBPs, Talent Acquisition, Enterprise Technology, Shared Services, and operational teams. Come in prepared, keep things moving, and leave with decisions made and owners named.
- Lead change management and adoption. Drive communication and adoption efforts tied to process redesign, work transitions, and new digital capabilities across the People organization.
- Influence through ambiguity. Help teams clarify roles, build new habits, and adopt more scalable ways of working during periods of high change.
Internal Consulting \& AI Fluency
- Act as an internal process consultant. Embed in People initiatives as a strategic thought partner — not just a documenter. Diagnose root causes, challenge assumptions, recommend redesigns, and help leaders distinguish between a process problem, a systems problem, and a change management problem.
- Streamline how the People team works. Identify friction in the team's internal operating model — handoffs, decision rights, communication loops, duplicated effort — and build infrastructure that helps the function operate with more consistency and less noise.
- Build AI fluency across the team. Stay current on relevant tools, run practical experiments, and help the broader People team develop the capability to work with AI in their day\-to\-day. Contribute to Rivian's internal AI learning program.
- 6\+ years of experience in People Operations, HR transformation, process excellence, management consulting, or related fields within complex, scaling organizations, with a track record of redesigning — not just documenting — cross\-functional processes.
- Strong working knowledge of Workday — not configuration ownership, but deep enough fluency in its data model, workflows, and capabilities that you can translate a process problem into a real Workday\-based solution and write requirements the Workday team can build from. You know what the system can and can't do without needing to build it yourself.
- Practical fluency with AI tools and agentic workflows. You understand how AI agents are built — prompting, tool/function calling, system integration — well enough to design real solutions and prototype where useful, then scope and hand off cleanly to engineering for production builds.
- Strong systems\-thinking capability, with the ability to connect strategy, operations, employee experience, data, and technology into clear and actionable recommendations.
- Strong facilitation skills: capable of running a workshop with skeptical stakeholders and arriving at a clear process map, agreed\-on requirements, and concrete next steps.
- Ability to move fluidly between strategic framing and hands\-on execution — you write the brief and do the work.
- Clear, concise written communication: you'll regularly translate operational complexity into technical requirements and stakeholder\-ready narratives.
- Experience partnering closely with IT, digital, systems, or engineering teams to define requirements, evaluate options, and prioritize work across platforms.
- Comfort operating in ambiguity. Rivian moves fast. The problems are rarely fully defined when you arrive.
PREFERRED QUALIFICATIONS
- Experience in high\-growth technology, EV, manufacturing, or similarly complex operating environments.
- Background in Lean, Six Sigma, design thinking, or service design applied to People or service\-delivery processes.
- Experience designing AI governance frameworks or responsible AI policies for HR applications.
- Prior experience supporting global HR operations, shared services, employee service delivery, or other scaled operating models.
- Experience supporting manufacturing, plant operations, or deskless/hourly workforce environments.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rivian AI Hiring
Rivian has 7 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Based in Palo Alto, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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