Internship, Systems Integration Engineer, Service; Fall

What To Expect

Consider before submitting an application:
This position is expected to start August or September 2026 and continue through the 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.

• 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
• Schedule 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

• 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 prediction 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
• Experie…