Morpheus Talent Solutions

Applied AI Researcher - Model Evaluation & Data Strategy

San Francisco (in-person preferred; open to remote across US, UK, Australia, and Europe) · Retained search - confidential client

The engagement

Morpheus has been exclusively retained to lead the search for a founding Applied AI Researcher on behalf of an early-stage, profitable AI research company.

About the client

An early-stage AI research company that works with leading AI labs to find where frontier models fail and build the expert human data that fixes them. They run a vetted network of 5,000+ top-1% specialists across finance, medicine, law, engineering, music, and other domains - competing on the quality of expert judgment, not the scale of cheap labeling.

Backed by a top pre-seed fund and angel investors who are founders and senior researchers at leading frontier AI labs. Already profitable.

The role

A founding, research-first seat - a genuine thought partner on evaluation and data strategy, not someone who coordinates other people's research, and not client-facing or delivery. You'll design evaluations, form and test hypotheses about model failure, and define the datasets, rubrics, reward signals, and quality controls that move performance - then work with engineers to turn them into scalable programs. Publishing is core to this role, not a perk - you'll be expected to author and present research that positions the company as a research partner to the field, so a prior publication record is essential.

What you'll do

• Design evaluations for generative, reasoning, tool-use, and agentic systems across modalities - text, audio, vision - where technique and domain expertise differ meaningfully by modality.
• Own real experimental design: hypothesize where a model breaks, build the eval to test it, and quantify what's actually failing.
• Build RL environments and reward signals in close partnership with engineers.
• Recommend SFT data, preference data, expert demonstrations, critiques, and eval sets.
• Build quality systems - calibration, blind review, adjudication - that hold up in non-deterministic domains.
• Run pilots that prove whether an intervention moves performance, and publish work that positions the company as a research partner to the field.

What the client is looking for

• A track record of published research - you've authored papers at venues like NeurIPS, ICML, ICLR, ACL, or EMNLP (or comparable). This is a research seat with a mandate to publish, so a demonstrated publication record is essential.
• Genuine experimental-design experience - you've designed studies and evals, not just executed someone else's rubric.
• Comfort operating in non-deterministic domains and with novel, fast-moving research frameworks.
• Agentic evaluation proficiency; RL environment experience, ideally built alongside engineers.
• Cross-modal understanding - awareness that audio, text, and vision each demand different techniques.
• Strong Python, model APIs, and structured datasets; solid grounding in benchmark design, human eval, rubric development, and statistical analysis.
• Familiarity with SFT, preference optimization, RLHF/RLAIF, reward modeling, synthetic data, or LLM-as-a-judge.
• Strong technical writing and the ability to drive ambiguous research independently.

Nice to have

Experience at an AI lab, foundation-model company, or post-training team; expert-data or human-eval program design; multimodal/coding/agentic eval work; public benchmarks or eval frameworks.

The reality - worth knowing up front

This is an early, high-momentum team that currently works a six-day week: Saturdays are fully remote and self-directed, no set hours - most people use them as a heads-down research day. Compensation is $200K-$350K base + equity; visa sponsorship available.

To apply

Apply here or message me directly. I represent this search exclusively and will share the company name, team, and full details confidentially with candidates who are a strong fit.