Resume Example & Template

AI Evaluation Engineer Resume Example

The engineer who decides whether a model is good enough to ship — and needs a CV that shows judgement, not just tooling.

Build your AI Evaluation Engineer resume

What is a AI Evaluation Engineer?

An AI Evaluation Engineer builds the tests that gate a model release. That means designing benchmarks that reflect the product rather than the leaderboard, building the harness that runs them automatically, deciding what counts as a regression, and knowing how far to trust an automated judge before a human has to look.

The role appeared because shipping decisions stopped being obvious. Once models are good at everything in general and unpredictable in particular, the only way to know whether a candidate is better is to measure it against your own use case. Public benchmarks rarely correlate with what users experience.

It sits between research and engineering. You need enough ML understanding to know why a model fails, enough software skill to build reliable infrastructure, and enough statistical care to avoid declaring victory on noise. Very few CVs demonstrate all three, which is why the role is hard to fill.

Key skills for a AI Evaluation Engineer resume

benchmark design
eval harness
LLM-as-judge
human evaluation
regression testing
error analysis
statistical significance
A/B testing
metric design
annotation quality
Python
PyTorch
CI/CD
experiment tracking
inference optimisation

AI Evaluation Engineer resume example

Marcus Feld

AI Evaluation Engineer

Berlin, Germany

Summary

Evaluation engineer who decides whether a model is good enough to ship. Built the harness gating every release at a 200-person AI company — 40+ benchmarks, offline and online. Caught two regressions that would have reached production and cut evaluation turnaround from four days to under three hours.

Experience

AI Evaluation Engineer · Halden AI

Jun 2023 – Present

Own the evaluation harness gating every model release: 40+ automated benchmarks across capability, safety and regression.
Cut evaluation turnaround from four days to under three hours by parallelising runs and caching deterministic scoring.
Caught two capability regressions pre-release that aggregate metrics missed, by adding task-level breakdowns.
Built the LLM-as-judge pipeline with human spot-checks and published agreement rates so teams know how far to trust it.

Machine Learning Engineer · Halden AI

Sep 2021 – May 2023

Trained and fine-tuned retrieval and classification models, owning the data pipeline end to end.
Built the first internal eval scripts — the work that became a dedicated evaluation function.

Education

MSc Computer Science

Technical University of Munich · 2016 – 2018

BSc Mathematics

University of Freiburg · 2013 – 2016

Certifications

Skills

Evaluation: Benchmark Design · LLM-as-Judge · Human Evaluation · Regression Testing · Error Analysis

ML Engineering: Python · PyTorch · Hugging Face · Fine-tuning · RAG

Infrastructure: CI/CD · Docker · Kubernetes · Weights & Biases · MLflow

Use this as your starting point

How to write a AI Evaluation Engineer resume that stands out

Lead with a shipping decision you influenced. "Caught two regressions before release" is the entire value of the role, stated in one line.
Show that you built an internal benchmark, not just ran public ones. Anyone can quote MMLU; the skill is knowing it does not predict your product.
Be explicit about LLM-as-judge and its limits — including the agreement rate with human raters. Reviewers are looking for calibrated trust, not enthusiasm.
Quantify the loop time. Evaluation that takes four days blocks a team; getting it to hours changes how often people can ship.
Mention statistical care once, concretely — significance, confidence intervals, sample size. It separates evaluation engineers from people who print averages.
Include the failure taxonomy work. Turning "the model is worse" into a breakdown by task type is the analysis that makes evaluation actionable.

AI Evaluation Engineer resume — FAQ

How is this different from a Machine Learning Engineer?

An ML engineer builds and improves models; an evaluation engineer decides whether an improvement is real. The skills overlap heavily, but the mindset differs — evaluation work is closer to testing and measurement than to modelling.

Do I need a research background?

Rarely. Strong software engineering plus statistical literacy and real curiosity about failure modes is usually enough. Many people move into evaluation from ML engineering, data science or QA.

Is "evals" a stable job title?

The function is stable; the title varies. You will see AI Evaluation Engineer, Model Quality Engineer, Applied Research Engineer (Evaluation) and simply "Evals". Use the wording of the ad you are applying to.

What should I show if I have never built an eval harness at work?

Build a small one publicly. Take an open model, define a task you care about, write the benchmark, and publish the results with the methodology. That is directly relevant evidence and very few applicants have it.

Ready to land your dream job?

Join all the job seekers who have successfully built their resumes and advanced their careers with CopilotResume.

Build Your Resume Now Browse Templates

Transparent and cost-effective pricing plans.