Anyone AI
via Ashby
Research Scientist (Remote/US/LATAM)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 70d ago
The date the source published, not the day we noticed it (2026-07-06). Last seen at its source 2h ago.
Is it remote?
Fully Remote, Ecuador - Fully Remote, Mexico - Fully Remote, Colombia - Fully Remote, Peru - Fully Remote, Argentina - Fully Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Available worldwide
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $108.3k–165.6k/yr
Middle 50% of 10 listings that do state pay — Operations · all levels · Worldwide · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
ashby employer's own board first seen 17d ago · last seen 2h ago
The listing
Research Scientist, LLM Evaluations & Benchmarking
Anyone AI Labs
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.