Research Scientist, LLM Evaluations & Benchmarking
Anyone AI Labs — Human Data Division
Reports to: CEO · Remote / LatAm / US
The role Evaluation is one of the hardest open problems in AI: we still don't have reliable ways to measure what frontier models can and can't do, and the field mostly runs on benchmarks that are saturated, contaminated, or measuring the wrong thing. You'll own that problem at Anyone AI, measuring frontier model capability.
This is a research role at heart: you decide what a good evaluation is , design the benchmarks that prove it, and defend the methodology under lab scrutiny. You'll build frontier-grade evaluation packages across reasoning, coding, agents, tool use, and multi-modal — grounded in expert-verified truth, validated against multiple models, and QC'd to survive buyer-side review.
Responsibilities
● Evaluation research. Turn eval targets into original benchmark designs. Own the hard measurement questions: construct validity, item discrimination, headroom, reliability, contamination, and capability elicitation. Push toward evals that stay informative as models improve.
● Benchmark development. Build evaluation packages with subject-matter experts, each with expert-verified ground truth, multi-model headroom results, and rigorous QC (calibration layers, severity-weighted rubrics, deterministic verifiers).
● Experts. Recruit, calibrate, and review a pool across coding, agentic/tool-use, and STEM/reasoning. Be the final arbiter of correctness and frontier difficulty.
● Lab relationships. Be a technical point of contact for labs, with CEO support. Understand what they're trying to measure and translate it into an evaluation design.
● Delivery & dissemination. Turn lab requests into winning sample packages and own pilots end to end. Where the work generalizes, help turn it into public benchmarks and papers: we support publishing at venues like NeurIPS Datasets & Benchmarks, ICLR, and ACL.
What we're looking for
● Research background in ML evaluation or benchmarking (a track record of published or open benchmarks, eval/measurement research, or equivalent hands-on work that labs have relied on).
● Deep LLM/frontier-model benchmarking expertise, with real strength in code-model and agentic evaluation.
● Fluency with the measurement problem itself: construct validity, psychometrics, rubrics, pass rates, headroom, contamination, and what makes a task genuinely discriminate a model.
● Interest in the safety side of evaluation, capability elicitation, robustness, and measuring the things that are hardest to measure honestly.
● Proven ability to hold a team or expert pool to a rigorous standard.
● Comfort with the full research loop: framing the question, running the study, and writing it up.
● Fluent English; Spanish a plus.
Skills Required
- Research background in ML evaluation or benchmarking (published/open benchmarks or equivalent)
- Deep LLM benchmarking expertise with strong experience in code-model evaluation
- Fluency with evaluation concepts: rubrics, pass rates, headroom, contamination, discriminative task design
- Proven ability to recruit, calibrate, and hold expert pools or teams to rigorous standards
- Fluent English
- Spanish
What We Do
Anyone AI is an edtech startup dedicated to bridging the AI talent gap by investing in software developers from Latin America. The company provides intensive, hands-on training programs in Machine Learning and Artificial Intelligence, led by industry experts. By combining technical skill development with employability support, Anyone AI prepares professionals for global career opportunities, helping them transition into high-impact roles within the rapidly evolving AI and technology sectors.
