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About This Role
Location
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Irvine, California; Palo Alto, California
Employment Type
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Full time
Location Type
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Hybrid
Department
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Technology and InnovationArchitecture, Systems, and Controls
Compensation
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- $135\.1K – $185\.8K • Offers Equity • Offers Bonus
The posted salary represents the lowest and highest ranges RV Tech reasonably and in good faith expects to pay for the position. Any posted salary range pertains only to the estimated starting pay for the role. Actual starting pay is based on a number of factors, including, but not limited to, the candidate’s experience, skillset, qualifications, specific competencies, relevant education, and location.
About Us
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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
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The Systems Design Reliability Engineering (SDRE) team is building the next generation of AI\-assisted, model\-driven systems engineering at Rivian VW Group — replacing heavyweight requirements processes with simulation\-first design, Digital Twin\-based verification and test coverage. We are a small, high\-leverage team, and we are looking for an engineer who wants to work at the intersection of AI tooling and physical system modelling.
You will develop and operate the AI tooling and Digital Twin infrastructure that underpins SDRE's cross\-domain methods — across Vehicle Controls, Infotainment, Communications, and Access. You will own feature(s) end\-to\-end: from building plant models and co\-simulation environments, to deploying LLM\-assisted requirement and test pipelines. You will also do real systems engineering — author requirements, perform analyses, design reviews, STPA, and apply SDRE methods hands\-on.
Responsibilities
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1\. Digital Twin Development \& Operation
- Build and maintain multi\-fidelity plant models (FMU\-packaged) for vehicle subsystems — powertrain, dynamics, thermal, body — using Python, OpenModelica, Julia, or Simulink.
- Develop and run co\-simulation environments (FMI\-based) that pair vECUs with plant models for three core use cases:
- Model\-to\-code — simulate vehicle behaviors against a plant to develop and validate control requirements before a line of production code is written.
- SIL regression tests — run SIL/virtual ECU controllers against plant models in nightly CI to catch regressions early and expand corner\-case coverage.
- Field issue replay — reproduce field failures in the digital twin, verify fixes virtually before shipping.
- Correlate models against real vehicle, dyno, and lab rig and fleet data
2\. AI\-Assisted Systems Engineering Tooling
- Build LLM pipelines for requirement drafting, test script generation, coverage gap analysis, and root cause analysis over SE artifacts.
- Deploy semantic search and RAG over requirements, architecture models, test scripts, using modern LLM app stacks (LangChain, LlamaIndex, or equivalent).
- Integrate AI assistants into GitLab and test management systems via APIs, plugins, and CI/CD pipelines.
- Build AI analytics tools that correlate requirements, architecture changes, and calibrations with fleet data, field issues and test failures — surfacing similar historical problems and candidate fault paths.
3\. Systems Engineering (Hands\-On)
- Author requirements and test cases as a practicing systems engineer — applying RequiTest and test\-driven SE methods.
- Participate in architecture, interface, and safety design reviews across domains.
- Document AI\-augmented SE process standards and playbooks; help drive adoption across programmes.
- Capture process patterns from domain teams and convert them into AI\-supported workflows, with human\-in\-the\-loop guardrails.
Qualifications
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Minimum Qualifications:
- BS/MS in Electrical, Computer, Mechanical, or Systems Engineering, or related field — or equivalent demonstrated experience through projects.
- Strong Python skills — data processing, prototyping AI workflows, automation scripts, or microservices.
- Practical experience building LLM applications: RAG pipelines, semantic search, structured reasoning, or agent frameworks.
- Systems\-minded: able to decompose a physical product into subsystems, behaviors and interfaces — and reason about how they interact.
- Comfort iterating quickly from prototype, to production, using modelling in a fast\-paced engineering environment.
Preferred Qualifications
- Experience with physical simulation tools: OpenModelica, Simulink, Julia, Modelica, or FMI/FMU\-based co\-simulation.
- Hands\-on projects involving physical systems — vehicle dynamics, powertrain, robotics, Baja SAE, Formula SAE, solar car, or similar.
- Familiarity with automotive SE artifacts: requirements, test cases, E/E architecture, CAN signals, DBC/ARXML.
- Experience with LLM evaluation: precision/recall, hallucination rate, latency, cost — for SE\-specific use cases.
- GitLab CI/CD experience;
- AI portfolio: projects applying LLMs or ML to engineering or technical documentation — even academic or personal projects count.
- Fault tree analysis, DFEMA or STPA know\-how
Total Rewards
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We build the exceptional — and we believe the people doing that work should be rewarded accordingly. In addition to a competitive base salary, full\-time positions may be is eligible to participate in our annual company performance bonus program.
Payments are discretionary and not guaranteed; actual amounts depend on company results and the terms of the plan in effect, and require active employment at the time of payout. This role is also eligible for equity in the form of Restricted Stock Units (RSUs), subject to board approval and the terms of our equity incentive plans, including applicable vesting requirements.
In addition to our compensation programs, we invest in our people with a comprehensive benefits package designed to support the health, wellbeing, and financial future for full\-time employees — including health coverage, retirement savings, time off, and family planning programs. Offerings vary by country. Learn more about our global benefit programs.
External candidates can apply for this role through the Rivian and Volkswagen Group Technologies careers site (https://rivianvw.tech/\#careers).
Equal Opportunity
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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
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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) record keeping; (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.
If you provide a mobile telephone number as part of your application or during the recruitment process, Rivian and Volkswagen Group Technologies may use that number to contact you via SMS text message for recruitment\-related purposes, including scheduling, logistics, and status updates. Message and data rates may apply. You may opt out of SMS communications at any time by replying STOP to any text message you receive from us. Consent to receive SMS messages is not a condition of applying for or being considered for employment.
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*Please note this job posting represents an open, active vacancy. Additionally, we are not currently accepting applications from third party application services.*
Compensation Range: $135\.1K \- $185\.8K
Salary Context
This $135K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $135K to $185K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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 Palo Alto, CA, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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