Senior Agent Evaluation Engineer, AI Evaluation Platform

Join Apple Services Engineering to define how we measure whether AI agents actually work. We are seeking a senior engineer who has built agents and knows how to evaluate them rigorously. You will design evaluation suites, build and validate judges, and ship agents on our evaluation platform, so the platform is shaped by real use and not guesswork. You will partner with researchers, product teams, and platform engineers to turn evaluation methodology into tools other teams can adopt. We are looking for builders who move quickly, iterate in the open, and can tell a trustworthy evaluation from a theatrical one. Were building the evaluation platform that will serve all of Apples generative AI and agent systems. Building agents got faster. Knowing whether they got better did not. This role closes that gap.

Youll build real agents and evaluate them: multi-turn trajectories, tool-use chains, non-deterministic outputs, and judge reliability. Youll write evaluation suites, validate judges against human labels, and measure variance across trials. Some agents you build will evaluate other systems (test-case generation, judging, failure triage). Others will be reference agents or built with partner teams. When the platform gets in your way, youll file the gap, and often fix it yourself.

This is a hands-on role with a lot of autonomy. Youll work in Python with a team of 7-12 engineers.Build and ship: Take ownership of features and services within the evaluation platform: APIs, SDKs, orchestration components, evaluation runners. Youll have the room to make calls on your own work and the support to deliver it well.

Productionize ML research: Partner with research engineers to take their prototype code and turn it into reliable services. Youll learn their world quickly and translate research patterns into clean Python that holds up under real load.

Move fast, responsibly: Youll get scoped problems with room to figure out the how. We trust you to balance speed with care, to know when something needs a quick prototype and when it needs a design doc, tests, and a careful rollout.

Improve as you go: Notice the rough edges and pick them up. The flaky test, the slow build, the confusing API, the runbook thats out of date. We want someone who leaves the codebase a little better every week.

Developer experience: Help build the SDKs and abstractions that other Apple teams use to evaluate their models and agents. Youll feel the friction of bad ergonomics directly, which puts you in a great position to fix it.

Operational ownership: Your code runs in production. You write the tests, set up the CI, add the metrics, and stay close when something breaks. You dont need to be an SRE, but you take care of what you ship.* 4-8 years of software engineering experience shipping production software.

• Hands-on agent builder. Youve built LLM agents beyond demos: tool use, MCP, multi-step workflows, context management, error recovery.
• Evaluation depth. Youve designed evaluations for agents, not just models. You understand non-deterministic outputs, multi-trial measurement (pass@k, variance), trajectory scoring, and scoring drift. Youve worked with tools like Inspect, Braintrust, LangSmith, or Promptfoo, or built your own.

Judge rigor. You know how to build an LLM judge, validate it against human agreement, and know when not to trust it. Fast from idea to prototype. You can point to things you built quickly, then iterated on or shipped. You can also tell us about a prototype you killed, and why. Fluent with AI coding tools. You use Claude Code or equivalents daily, including skills, slash commands, and agent-style workflows. Python as primary language. Strong with FastAPI, Pydantic, and the ecosystem. Your code is clean, tested, and easy for the next person to pick up. Judgment on speed vs. discipline. You know when a quick prototype is enough and when you need a design doc, tests, and a careful rollout. Cross-team collaboration. Youve worked closely with researchers, product teams, or platform engineers. You write clearly and flag blockers early.Experience working on developer platforms, internal tools, or SDKs Production experience with LLM/agent systems - building, evaluating, or operating them Familiarity with job orchestration frameworks (Temporal.io, Airflow, or similar) Distributed compute experience (Ray, Dask, or Kubernetes-based job systems) Experience with experiment tracking or ML lifecycle tooling (Weights & Biases, MLflow, etc.) Startup or early-stage experience where you wore multiple hats and shipped under constraint