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About This Role
About Us:
Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world.The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone. Role Summary:
We're looking for an AI Product Engineer to build and ship enterprise tools, systems and automations that drive measurable operational savings or improvements across Rivian's Facilities organization. You'll use AI-native development tools like Cursor, Gemini, and Claude to turn real Facilities problems into working solutions that save time, reduce cost, and eliminate manual overhead. You'll embed directly into Facilities workflows — construction project reporting, capital program tracking, real estate operations, building management — and build automations that have a tangible dollar-and-cents impact. Responsibilities: Build & Deploy
- Build internal applications, AI agents, and workflow automations using LLM-assisted development tools (Cursor, Gemini, Claude, Glean, etc.) targeting specific Facilities pain points — manual reporting, data entry, schedule tracking, cost reconciliation — with a focus on quantifiable time and cost savings
- Stand up no-code and low-code solutions, maintainable by non-technical team members where speed and simplicity are prioritized over engineering complexity
- Connect Facilities data sources (ACC, Procore, Kahua, FOS, Databricks) via APIs to support intelligent workflows that unify siloed information
- Own the Full SDLC by applying traditional product development rigor to AI-generated code. You will manage sprint cycles, define technical requirements in Jira, and oversee the end-to-end lifecycle of the tools you build.
- Conduct code reviews on peer-built AI tools and own enhancement of deployed solutions based on user feedback and evolving business needs
Learn the Business
- Rapidly build working knowledge of Facilities operations — how capital projects are planned, budgeted, tracked, and delivered — by shadowing project managers, construction teams, and operations staff to find automation opportunities that aren't on anyone's roadmap yet
- Understand the Facilities tech stack well enough to identify where AI can eliminate friction and reduce costs
- Document solutions, create reusable templates, and contribute to internal playbooks and how-to guides
Collaborate & Deliver Value
- Partner with Facilities stakeholders to understand workflows, quantify inefficiencies, and prioritize automations with the highest operational ROI
- Translate operational needs into working prototypes and iterate based on user feedback until adoption sticks
- Help track and report the impact of deployed solutions — hours saved, errors reduced, costs avoided, scaling without new headcount
Qualifications: Required Qualifications & Skills
- 4+ years in a technical role — software development, product ownership, technical program management, or similar
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field; OR equivalent practical, hands-on experience in lieu of a degree
- Demonstrated experience building with AI-assisted coding tools (Cursor, Codex, Claude Code, or equivalent)
- Working knowledge of prompt engineering, AI agent design, and LLM application development
- Strong foundational knowledge of Agile methodologies, version control (Git), code reviews, and secure deployment pipelines.
- Strong UI/UX instincts — can produce functional, clean interfaces without a design team
- Strong communication skills; equally comfortable in a whiteboard session with leadership or a working session with project teams
- Self-directed; thrives in ambiguous environments where the playbook doesn’t exist yet
Preferred Qualifications
- Familiarity with large-scale construction, real estate, or capital programs
- Experience with enterprise AI tools like Glean or similar knowledge management platforms
- Exposure to MCP (Model Context Protocol) frameworks and multi-agent architectures
- Prior experience shipping internal tools in a non-engineering business unit
- Track record of upskilling peers or running internal training on new technologies
#LI-Hybrid Pay Disclosure:
The salary range for this role is USD 98,500 - 130,480 for Georgia based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs.
The successful candidate may be eligible for annual performance bonus and equity awards.
We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com.
You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than November 30, 2026. Company Statements: Total Rewards
Total compensation packages for this position include base salary, eligibility for an annual performance bonus, and eligibility for equity.
In addition, our benefits package has been designed to support the health and wellness of our employees. Benefit offerings include Flex Time Off, retirement savings plans as well as medical, vision and dental coverage. For more information on RV Tech’s comprehensive benefits package for full-time employees, check out our Global Benefits Site.
External candidates can apply for this role through the RV Tech Careers site (https://rivianvw.tech/#careers). If you are a current employee, please apply through our internal job board (https://internal-careers-rivian.icims.com/).Equal Opportunity
Rivian and Volkswagen Group Technologies is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. We are also committed to ensuring compliance with all applicable fair employment practice laws regarding citizenship and immigration status.
Rivian and Volkswagen Group Technologies is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at [email protected].Candidate Data Privacy
Rivian and VW Group Technologies (“Rivian and Volkswagen Group Technologies”) may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian and Volkswagen Group Technologies may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law.
Rivian and Volkswagen Group Technologies may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian and Volkswagen Group Technologies affiliates; and (iii) Rivian and Volkswagen Group Technologies’ service providers, including providers of background checks, staffing services, and cloud services.
Rivian and Volkswagen Group Technologies may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions.
Please see our Candidate Data Privacy Notice (English) and Candidate Data Privacy Notice (Serbian) for more information. Please note this job posting represents an open, active vacancy. Additionally, we are currently not accepting applications from third party application services.
Salary Context
This $98K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 and Volkswagen Group Technologies, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($114K) sits 46% below the category median. Disclosed range: $98K to $130K.
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 and Volkswagen Group Technologies AI Hiring
Rivian and Volkswagen Group Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $130K - $130K.
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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