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

Description:
Consider before submitting an application:
This position is expected to start August or September 2026 and continue through fall term (ending approximately December 2026) or continuing into Winter/Spring 2027 if available and there is an opportunity to do so. We ask for a minimum of 12 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 work 40 hours per week on-site. Many students will be limited to part-time during the academic year.
Tesla Service is integrating 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 Service Ops. 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.

Responsibilities:
Inventory AI tools, agents, and workflows across Service

Ensure consistent performance of AI tools across regions and personas — same input, same quality of output

Identify and eliminate redundant automation so that one capability is owned by one tool, not three

Redefine roles and SOPs into digital format so AI tools can plug into real workflows instead of sitting on the side

Build in-tool feedback capture directly into agents usage (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's duplicated

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 feedback ships an improvement

Partner with engineers and program leads to reduce onboarding friction — pre-configured access and skills, sensible defaults, in-context hints

Scheduled training sessions and roundtable for user question

Identify high-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 security and compliance standards (data handling, access control etc)

Define and monitor a three-layer success framework:  Adoption, Quality, Impact

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

Requirements:
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 to design reliability and reversibility

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

Familiarity with one major 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 & 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 + benefitsPay 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.