Senior Applied AI Engineer

About the role

Applied AI Engineer

Location: Onsite 5 days a week in Austin, TX, relocation is offered.

Level is a learning technology company dedicated to helping students build real academic and life skills with confidence and joy. We combine proven curriculum principles with world class interactive design to make meaningful practice something students want to come back to, not something they struggle through.

We support what teachers, schools, and parents are already doing by increasing student engagement with high quality, standards aligned practice that reinforces classroom learning. That’s why we’re building:

For students: Rewarding and motivating learning experiences that meet students where they are to build real academic proficiency.

For educators: Tools that fit naturally into instruction and help students stay engaged while reducing teacher workload.

For parents: Activities that help your kid catch up or get ahead, build confidence, and minimize homework battles, whether learning at school or at home.

We are committed to helping every person maximize their potential and live a life of meaning. It’s a difficult problem that requires brilliant people and tremendous effort over time.

What You'll Do:

- Ship production agentic systems- Design and build agents and agentic workflows that solve a defined problem end to end. You own prompts, tools, retrieval, guardrails, observability, cost and latency budgets, and rollout.

- Automate SME workflows- Identify high-leverage operational toil (review pipelines, content QA, labeling and ops loops, support triage, internal copilots) and partner with the SMEs running those workflows to define success criteria, validate outputs, and replace meaningful chunks of that work with AI systems they trust. SMEs are co-owners of quality from the start of the project.

- Own the evaluation loop and the golden datasets that anchor it- Build offline evals, LLM-as-judge with calibration, regression suites, and online metrics. Maintain versioned, decontaminated golden datasets covering intents, difficulty, edge cases, and adversarial inputs, and continuously enrich them with real production failures and SME-validated labels. The measurement plan is what decides whether a feature ships.

- Make AI features safe- Treat what you deliver as a regulated product. Design for relevant compliance frameworks (e.g., COPPA, FERPA) from day one, run safety and bias evals before launch and continuously after, and build the human-in-the-loop and content-filtering controls AI features need before they reach end users.

- Hand off what you ship- Production features leave your hands with documentation, runbooks, an eval harness, and dashboards. An embed is not complete until the receiving team has shipped a change to the system without you in the room. Typical embed length is 4 to 12 weeks.

- Make AI features easier for the rest of engineering to build- Internal libraries, patterns, and playbooks so other teams can ship AI features without your direct involvement.