Tesla AI's Agentic Tooling team builds the harness and skill ecosystem for the AI org powering FSD and Optimus. Our agents already do real engineering work day-to-day for engineers across the org. We own the customized harness end-to-end: the orchestration around model backends, the tool ecosystem agents call into, the session and memory layer, the observability stack, and the evaluation system that gates every prompt, skill, and model upgrade.
You'll work across the full surface of the team, extending the harness, shipping new skills, building evaluations, owning observability, and picking up whatever is most leveraged each quarter. Models and skills evolve weekly, so a fast iteration loop and honest measurement of what you ship are non-negotiable.
Harness development: Extend the agent runtime and tool ecosystem end-to-end. Add new tools, integrations, and backends without regressing existing skills, and keep the platform easy to extend as the ecosystem evolves
Skill development: Ship new skills that solve real workflows for engineers across the AI org, from one-off automations to the agent capabilities that get reused across the fleet
Evaluation: Build and operate the evaluation framework that catches regressions in prompts, skills, harness changes, and model upgrades before they reach users
Observability and reliability: Own session-level observability across our tracing, logging, and metrics stack. Build the dashboards and alerting that tell us when an agent is broken before users do
Production infrastructure: Operate our production agent services on Kubernetes, async backend services, durable storage for session memory and embeddings, and the public-facing APIs that route work to them
Contribute to architectural decisions: With a focus on security, scalability, and reliability, especially around credential isolation and the seams between our harness, the model runtime, and the sandboxing layer
Embed with users and ship cross-team, end-to-end: This is a collaboration-heavy role. You'll sit with engineers across the AI org to understand their workflows, identify the moments where an agent could remove toil, and drive the solution from the first conversation through prototype, rollout, and hand-off. Many of our highest-impact tools start as zero-to-one builds where you step in, learn enough of a stakeholder's domain to understand what "done" looks like, and ship the first version yourself
Proficiency with Python; Go is a plus
Strong foundation in Linux systems. Containerization and Kubernetes experience are a plus
Strong foundation in concurrent and async programming
Experience with relational and vector data stores (session memory and embeddings back our long-running agent workflows) is a plus
Open-ended problems and product instincts: You enjoy talking to engineers across teams, can convert a vague ask into a shipped solution end-to-end, and bias toward a usable v1 that people adopt over a "complete" solution that takes months to set up
Adaptability and curiosity in a fast-paced space: Agentic tooling moves weekly. You stay current on new models, frameworks, and agent patterns as a matter of habit, and you can re-platform your own work when the right answer changes underneath you
Experience with the Claude Code SDK, ACP (Agent Client Protocol), or opencode is a plus
Experience with sandboxing techniques and application security, sandboxed code execution, container isolation, capability-based access control, credential isolation, secrets management, is a plus
Experience building or operating an evaluation pipeline for skills, prompts, or models, offline replay, scoring against ground truth, regression gating in CI, or comparable work, is a plus
Experience with CI/CD pipelines for ML or agent systems, distributed task orchestration, or developer-tools work where the customer is another engineer is a plus
Benefits
Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
Expected Compensation
$140,000 - $390,000/annual salary + cash and stock awards + 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.