Internship, Systems Integration Engineer, Tesla Service (Fall 2026)

What to Expect

Consider beforesubmittingan application:

This position is expected to start August or September 2026 and continue throughfall term (endingapproximately December 2026) or continuing into Winter/Spring 2027 if available and there is an opportunity to do so. We ask for a minimum of12 weeks, full-time (40 hours/week) and on-site, for most internships.Our internship program is for students who are actively enrolled in an academic program.Recent graduates seeking employment after graduation and not returning to school should apply for full-time positions, not internships.

International Students: If your work authorization is through CPT, please consult your school on your ability to work 40 hours per week before applying. You must be able to work40 hoursper week on-site. Many students will be limited to part-time during the academic year.

Tesla Serviceisintegrating AI tools - LLM assistants, copilots, internal agents, and self-service data platforms - into the daily work of advisors, technicians, mobile service, field engineers, and back-office teams. Adoption is accelerating across regions, and the next step is making it consistent, secure, and self-sustaining.

As the AI Adoption Intern, you will own the human and operational side of AI rollout across ServiceOps. You will work directly with frontline teams, development and ML engineers, and program leads to standardize how AI is used, build feedback loops that make agents self-improving, and turn pilots into production-reliable, easy-to-use tools at scale.

What You'll Do

• Inventory AI tools, agents, and workflows across Service

• Ensureconsistent performance of AI tools across regions and personas - same input, same quality of output

• Identifyandeliminateredundantautomation so that one capability is owned by one tool, not three

• Redefine roles and SOPs into digitalformatso AI tools can plug into real workflows instead of sitting on the side

• Build in-tool feedback capture directly intoagentsusage (thumbs, structured tags, free-text, local memory)

• Run periodic skill and agent audits - what each agent can do, where it fails, what's stale,what'sduplicated

• Establish a self-evolve path: feedback eval prompt/skill/tool update re-deploy, with the loop owned by the tool, not a human triage queue

• Close the loop with users when their feedbackshipsan improvement

• Partner with engineers and program leads toreduce onboarding friction - pre-configured access and skills, sensible defaults, in-context hints

• Scheduled training sessions and roundtable for user question

• Identifyhigh-leverage workflows where AI saves the most time and target them from pilot to production-grade reliability, best efficiency of cost, and agent quality

• Ensure scaled tool meets information securityand compliance standards (data handling, access controletc)

• Define andmonitora three-layer success framework: Adoption, Quality, Impact

• Build a success monitorthat surfaces regressions, flags adoption stalls, and triggers action when metrics drop, so leadership sees signal, not noise

What You'll Bring

• Currently pursuing a degree in Computer Science, AI/ML, Software Engineering, or a closely related technical field

• Efficient in the AI implementation layer - building, integrating, and shipping AI-powered features (not just using them)

• Strong understanding of LLMs and how they work - prompt design, context engineering, and harness engineering (tool use, agent loops, evals, guardrails)

• Solid grasp of AI design principles: when to use an agent vs. a workflow, when to use retrieval vs. fine-tuning, how todesignreliability and reversibility

• General data mindset - comfortable reasoning about datasets, evals, metrics and predication mechanisms (regression and supervised learning)

• Familiaritywith onemajor AI framework and understanding of MCP, agent tool design, or skill/plugin architectures

• Strong written and verbal communication - can explain a technical AI system to a non-technical operator clearly

• Experience with real life or production experience shipping AI-developed applications - i.e., AI features that real users depend on, not just demos or coursework preferred

• Experience with eval frameworks, prompt regression testing, or AI accuracy testing preferred

Compensation and Benefits Benefits

As a full-time Tesla Intern, you will be eligible for:

• Medical plans > plan options with $0 payroll deduction
• Family-building, fertility, adoption and surrogacy benefits
• Dental (including orthodontic coverage) and vision plans. Both have an option with a $0 payroll contribution
• Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Medical Plan with HSA
• Healthcare and Dependent Care Flexible Spending Accounts (FSA)
• 401(k), Employee Stock Purchase Plans, and other financial benefits
• Company Paid Basic Life, AD&D, and short-term disability insurance (90 day waiting period)
• Employee Assistance Program
• Sick and Vacation time (Flex time for salary positions), and Paid Holidays
• Back-up childcare and parenting support resources
• Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
• Commuter benefits
• Employee discounts and perks program

Expected Compensation $28.55 - $47.96/hour + benefits

Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.