Recruits Lab places AI research engineers and applied scientists at frontier labs, AI-native startups, and applied AI teams. We source from PhD pools, top university labs, and the small set of researchers who have shipped models in production. Average hire time: 14 days.
AI research engineers are recruited by specialist AI search firms, by in-house research recruiting teams at frontier labs, and through academic and publication networks. The pool is small and mostly passive: researchers with first or co-author papers at NeurIPS, ICML, ICLR, ACL, or CVPR who also ship production code. Reaching them requires technical outreach about the actual research problem, not a job description.
Recruits Lab recruits AI research engineers, applied scientists, and research scientists for foundation model labs, applied research teams, and research-led product companies, including confidential searches for stealth labs under NDA.
The best foundation model recruiters can tell pretraining, post-training, and evaluation work apart, and can talk credibly about compute, data, and publication policy. Foundation model hiring fails on three things: an unclear research charter, no compute story, and a loop that tests generic engineering. Use these criteria when comparing firms.
Recruits Lab runs foundation model and research engineering searches on this model, from pretraining and post-training work to evaluation and research infrastructure, on subscription, contingency, or retained terms with a 90-day replacement guarantee.
Use a generalist firm when the role is a standard software engineering hire with a clear title match. Use a specialist when the hire is judged on research contribution, when the compensation benchmark is a frontier lab rather than a local market band, or when the candidate's decision turns on compute, autonomy, and publication rights. Generalist loops usually screen the wrong signal and lose finalists on those three levers.
Research engineering is the bridge between academic ML and production AI. The people who do it well are rare — they can read a NeurIPS paper, reproduce it, modify it for a real product constraint, and ship it inside a six-week sprint. The market for this profile in 2026 is brutal.
Recruits Lab specializes in AI research engineer search for foundation model labs, applied research teams at AI-native startups, and research-led product teams. We map the actual pool: researchers with first or co-author publications at NeurIPS, ICML, ICLR, ACL, CVPR who have also shipped code into production.
We focus on three profiles: research engineers who blend engineering and research, applied scientists who own model layers of a product, and research scientists who lead novel work. Each requires different sourcing channels and different storytelling. We run all three.
The pool is small and the candidates are deeply networked. They will not respond to generic outreach. We use technical context, founder voice, and credible problem framing to start conversations that lead to interviews. Most placements are not actively looking.
We work with stealth foundation model companies under NDA, with venture-backed applied AI labs, and with research-led product teams. Our subscription model fits multi-role research org builds. Our flat contingency option fits one-off senior research hires.
Why this market is hard to recruit for. And how we solve each one.
Strong research engineers usually have first or co-author publications at top venues. We screen on real contribution, not just author lists.
Senior research engineers at Anthropic, OpenAI, and DeepMind are at $700K-$1.5M total comp. Smaller labs close on research autonomy, compute access, and equity upside.
Research engineers will not join a company that cannot give them GPUs. We surface compute strategy in the first call.
A large share of research talent is on H-1B, O-1, or in OPT windows. We screen on status early.
Some candidates want 100 percent research. Some want 70/30. Misalignment kills offers. We calibrate per candidate.
Stealth labs cannot let researchers publish. Some candidates will not move if they cannot publish. We surface this in the first conversation.
The repeatable system behind our 14-day average hire time.
Call covering research charter, compute budget, publication policy, team composition, and the specific technical problems the team is working on.
Map of researchers from frontier labs, top university groups, and applied research teams. Built from publication history, GitHub activity, and conference attendance.
Outreach written with enough technical specificity that researchers can tell the founder actually understands the work. Response rates 30-40 percent on the right pool.
Pre-screen on research direction, production experience, and motivation. Finalists arrive with publication summary and ship-history.
Many research closes hinge on compute and research autonomy. We manage those conversations alongside comp.
Original observations from live searches in this specialty.
Foundation model work — pretraining, post-training, alignment, data curation at scale, and evaluation of frontier capability — draws from a much smaller pool than AI research broadly. Candidates are usually identified through specific paper lines and lab affiliations rather than job titles, and they screen employers on compute access and publication policy before compensation ever comes up.
Comparatively few organizations pretrain from scratch. Far more need engineers who can run supervised fine-tuning, preference optimization, reward modeling, and rigorous evaluation on top of an existing base model. Scoping a role as post-training rather than generic research widens the qualified pool and shortens the search considerably.
Research-track candidates ask early whether they can publish, present, and open-source. Companies with an unwritten or restrictive policy lose finalists late in the process. Putting the policy in writing before the loop starts removes one of the most common causes of a failed close.
Research velocity is usually limited by training and evaluation infrastructure rather than by ideas. Teams hiring research engineers without a corresponding investment in the compute layer see slow ramp and early attrition, which is why these searches are frequently paired with an AI infrastructure hire.
Directional 2026 U.S. base salary estimates for venture-backed companies. Excludes equity, bonus, and sign-on. Confirm with current market data before extending offers.
| Role | Base Salary Range |
|---|---|
| Research Engineer (mid) | $220K–$320K |
| Senior Research Engineer | $300K–$450K |
| Staff Research Engineer | $420K–$600K |
| Applied Scientist | $240K–$380K |
| Research Scientist | $280K–$480K |
| Director of Research | $420K–$700K |
Sources: Levels.fyi, Pave 2026 data and Recruits Lab placement benchmarks. Frontier lab total comp frequently exceeds $1M. Directional only.
Quick answers to the questions founders and hiring leaders ask most.
Hiring an AI research engineer in 2026 requires a credible research charter, real compute budget, and a clear publication policy. The talent pool is publication-driven, so outreach must be technically literate. Recruits Lab averages 14 days from kickoff to signed offer on focused research searches.
A senior AI research engineer typically costs $300K–$450K base, with total comp at $500K–$900K at venture-backed labs and frequently exceeding $1M at frontier labs. Smaller labs close on research autonomy and equity, not by matching base.
An AI research engineer bridges academic ML and production AI. They reproduce and extend novel methods, design experiments, build training pipelines, and ship research into product. Strong candidates have both first-author publications and production ship history.
An ML engineer optimizes and deploys known methods. A research engineer designs new methods and validates them empirically. Publication record, novelty of work, and the share of time spent on experiments versus shipping are the typical dividers.
Yes. Stealth foundation model and applied research searches are a core practice area. We sign NDAs and run discreet outreach without disclosing the client name until late-stage finalists.
Pre-launch lab needed a Staff Research Engineer with both publication record and production training experience. Could not disclose company name until offer stage.
Recruits Lab ran the search under NDA. Mapped 60 candidates from frontier labs and top university groups. Used technical outreach referencing the research direction, not the company name.
Signed offer in 31 days from kickoff. Hire led training infrastructure work that became the lab's first public release.
"Most recruiters cannot tell the difference between a research engineer and an ML engineer. Recruits Lab can. That alone saved us weeks of bad slates."
"They ran a stealth research search for us under NDA without leaking the company name. Closed the role in a month."
Yes. We support pre-launch and post-launch frontier labs on senior research and engineering hires under NDA.
We screen candidates on publication preference and IP tolerance during the first call. Misalignment is usually the reason offers fall through; we surface it early.
Yes. We source by publication history, advisor lineage, and conference attendance for subfields including RLHF, alignment, mechanistic interpretability, multimodal, and inference optimization.
We focus on U.S.-based and U.S.-relocating talent. For pure ex-U.S. searches we recommend a regional partner.
90-day replacement guarantee on every placement. Research searches included.
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