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About This Role
At Mercedes\-Benz Research \& Development North America (MBRDNA), we are committed to delivering world\-class automotive technologies that push the boundaries of what is possible. Our teams of highly skilled engineers and designers use cutting\-edge software and technology, to enhance the driving experience and reduce environmental impact.
The AI Program Manager Intern will bridge the gap between technical AI development and project execution. The primary purpose of this role is to drive AI literacy, optimize engineering workflows, and boost overall team productivity. You will collaborate closely with Product Managers (PMs) and Developers to integrate cutting\-edge Large Language Models (LLMs) and agentic developer tools into daily operations, helping the team work smarter and faster.
This internship may commence at any point during the summer, beginning July 6, with at least a commitment until the end of the year.
### Job Responsibilities:
- AI Awareness \& Enablement: Research, champion, and conduct internal sessions on the latest LLM capabilities and developer tools to foster a culture of AI adoption.
- Workflow Optimization: Evaluate current development and management workflows to identify bottlenecks; propose and implement AI\-driven automation solutions.
- Cross\-Functional Collaboration: Serve as a liaison between PMs and Developers, ensuring technical requirements and product roadmaps align seamlessly.
- Tool Integration \& Best Practices: Help teams implement advanced coding assistants and open\-source protocols (like MCP) to streamline code generation, testing, and documentation.
- Project Tracking: Assist in managing sprint cycles, documentation, and progress tracking using project management software.
### Minimum Qualifications:
- Currently pursuing or recently completed a degree in Computer Science, Data Science, Engineering, Management Information Systems (MIS), or a related technical field.
- Prior experience (including internships, academic projects, or bootcamps) in software development or technical project coordination.
- Pursuing a Bachelor’s or Master’s degree in Computer Science, Engineering, or a highly related technical/analytical discipline.
- Strong foundational knowledge of modern LLMs (e.g., Claude, GPT, Gemini models) and their enterprise use cases.
- Hands\-on familiarity with next\-generation developer tooling (e.g., Claude Code, GitHub Copilot).
- Excellent communication and organizational skills, with the ability to explain complex technical concepts to non\-technical stakeholders.
- A proactive, problem\-solving mindset with a passion for operational efficiency.
### Preferred Qualifications:
- Prior professional work experience as a Software Developer or Engineer.
- Proficiency with standard agile project management and collaboration tools, specifically Jira and Confluence.
- Familiarity with the Model Context Protocol (MCP) for connecting LLMs to data sources and development environments.
- Basic scripting skills (e.g., Python) to help automate internal workflows.
Benefits/Perks:
- PTO
- Sick Time
Additional Information:
The current hourly rate for this position is as follows and may be modified in the future: $28 (Undergraduate Students)/$32 (Graduate Students) Why should you apply?
Here at MBRDNA, you create digital ecosystems around cars, you design a language between humans and machines, you make a car even more intelligent \- you make the new reality for cars. MBRDNA was honored as one of the "Best Places to Work" by BuiltIn in January 2024, a testament to our commitment to creating an exceptional work environment. At each of our offices, we foster a culture of collaboration and continuous learning, ensuring every team member can thrive and innovate. Benefits for Full\-Time\* Employees Include:* Medical, dental, and vision insurance for employees and their families
- 401(k) with employer match
- Up to 15 company\-paid holidays
- Paid time off (flexible time off for salaried employees), sick time, and parental leave
- Tuition assistance program
- Wellness/Fitness reimbursement programs
- Internships \& Contractors excluded from Full\-Time Employee benefits
MBRDNA is an equal opportunity employer (EOE) and strongly supports diversity in the workforce. MBRDNA only accepts resumes from approved agencies who have a valid Agency Agreement on file. Please do not forward resumes to our applicant tracking system, MBRDNA employees, or send to any MBRDNA location. MBRDNA is not responsible for any fees or claims related to receipt of unsolicited resumes.
Mercedes\-Benz Research and Development North America, Inc.PRIVACY NOTICE FOR CALIFORNIA RESIDENTS
https://mbrdna.com/california\-employee\-privacy\-notice/
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Mercedes-Benz Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Mercedes-Benz Group AI Hiring
Mercedes-Benz Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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