Leading Recruiting Firms for Deep Tech and AI Sectors (2026)

Hiring a research scientist is not the same as hiring a full-stack engineer, and the firms that excel at one rarely excel at the other.

Deep tech — the category covering artificial intelligence, quantum computing, biotech, robotics, advanced materials, and semiconductors — has become the most competitive talent market of the decade. A single staff research scientist at a frontier AI lab can command compensation north of a million dollars a year, and the shortlist of people capable of doing the work is measured in the hundreds, not thousands.

This guide walks through the recruiting firms that have built real networks in these sectors, how they charge, and when you should hire a specialist versus a generalist.

What Makes Deep Tech Recruiting Different

Deep tech hiring looks very little like standard technology recruiting. Three factors change the game.

Candidates are credentialed differently. A meaningful share of the talent pool holds PhDs from a narrow set of labs — think Stanford AI Lab, MILA, Berkeley BAIR, DeepMind, FAIR, or the handful of quantum programs at MIT, Caltech, and Delft. Recruiters without relationships in those communities struggle to get introductions, let alone close candidates.

Skills are extremely narrow. Within AI alone, a reinforcement learning researcher is not interchangeable with a large language model post-training engineer, and neither is interchangeable with a computer vision specialist. Generic keyword searches on LinkedIn miss these distinctions entirely. A good deep tech recruiter can tell you who trained which model at which lab and whether they are open to leaving.

IP sensitivity is higher than in typical tech hiring. Many deep tech searches involve research directions the company does not want competitors to learn about. Recruiters must run confidential searches, mask the employer brand until late in the process, and vet candidates for conflicts with prior employers. This is closer to the discretion required in CEO and executive search than it is to volume engineering hiring.

Top Firms Specializing in AI and Deep Tech

No single firm owns deep tech recruiting, but a handful have built genuine reputations in the space. The list below focuses on firms that regularly place senior AI, ML, and deep tech talent, drawing on their publicly disclosed work.

Daversa Partners

Daversa Partners is arguably the most visible name in venture-backed tech recruiting, and over the last several years they have built out a dedicated AI and deep tech practice. They work closely with top-tier VCs and tend to run searches for founding engineers, heads of AI, and early executive hires at fast-moving labs and applied AI companies.

True Search

True Search runs a deep technology practice that covers AI, infrastructure, and climate tech. Their strength is the combination of retained search rigor with a data platform called Thrive, which keeps long-term relationships with technical leaders warm across multiple search cycles.

Riviera Partners

Riviera Partners is the closest thing the industry has to a pure engineering and product executive search specialist. They focus exclusively on VP Engineering, CTO, and Chief AI Officer searches, which makes them a strong choice when you are filling leadership — less of a fit for individual contributor research roles.

Andiamo Partners

Andiamo Partners built their reputation on deep technical recruiting across finance and quant, and they have extended that muscle into AI research and infrastructure roles. They are a strong pick when your ideal candidate sits at the intersection of quantitative research and modern ML.

ZRG Partners

ZRG Partners is one of the largest talent advisory firms globally and runs a technology practice that covers AI, cybersecurity, and advanced hardware. Their scale is useful for searches that span multiple geographies, and they have published thought leadership on AI leadership hiring trends.

Other Notable Firms

Beyond the names above, a few other firms show up repeatedly in deep tech and AI mandates: Indigo Partners for product and engineering leadership at high-growth AI companies, FirstSearch for founding engineer and early-stage deep tech hires, and Caldwell for life sciences and biotech research leadership. These firms are smaller than the big five but often win specialized mandates that the larger shops are too broad to serve.

Boutique Firms vs the Big Six

The Big Six of global executive search — Korn Ferry, Heidrick & Struggles, Spencer Stuart, Egon Zehnder, Russell Reynolds, and Odgers Berndtson — all have technology practices, and several have spun up AI-specific verticals. They bring scale, board-level relationships, and the ability to run complex international searches. For a Chief Scientist or Chief AI Officer search at a public company, they remain a safe default.

Boutique firms win on a different axis. A ten-person firm focused on AI research hiring will usually have deeper day-to-day relationships with the specific labs you are targeting. They move faster, their partners typically run the full search themselves rather than handing off to junior associates, and their fee structures are often more flexible for earlier-stage companies.

The heuristic we recommend: if you are hiring an executive who will report to the board, the Big Six are worth the premium. If you are hiring the person who will actually build the model, boutiques almost always deliver better candidates.

Fees and Engagement Models

Deep tech recruiting fees track the broader executive search market but skew slightly higher because of the talent scarcity.

• Retained search — 30-35% of year one total compensation, paid in three installments (engagement, shortlist, placement). Standard for leadership and senior research roles.
• Container search — a discounted flat upfront fee plus a success fee on placement. Increasingly common for early-stage companies hiring their first head of AI.
• Contingency — 20-25% of first year base salary, paid only on placement. Used for individual contributor roles where multiple firms compete.

On a $400,000 total comp staff ML engineer role, expect retained fees of $120,000 to $140,000. On a $700,000 total comp VP of AI, expect fees of $210,000 to $245,000. Fees climb quickly when you move into the frontier lab compensation bands, which is one reason companies increasingly blend external search with internal sourcing tools.

For a parallel view of how these numbers compare in the go-to-market world, our breakdown of top sales recruiters for AI companies covers the same territory on the commercial side of the house.

Common Deep Tech Roles They Fill

Deep tech firms are asked to fill a specific set of roles that appear over and over across AI labs, biotech companies, and frontier hardware startups.

Machine learning engineers sit between research and production. They take a paper or an internal prototype and turn it into a system that serves millions of requests. Strong candidates have both publication history and shipping experience — a rare combination.

Research scientists focus on novel model architectures, training methods, or scientific problems that have not been solved. This is the population most closely tied to top PhD programs and frontier labs. Compensation packages are driven by publication record, citations, and specific model contributions.

AI safety researchers have become their own category, with demand outpacing supply as frontier labs and policy organizations build out alignment teams. Expect to pay a premium and to compete against Anthropic, DeepMind, and OpenAI for the same shortlist.

Founding engineers at early-stage deep tech companies need to span research and engineering. They are builders who can read papers, reproduce results, and ship production code, usually without a research team to lean on. These hires make or break the first 18 months of a company, which is why founders invest so heavily in getting them right.

Applied scientists and research engineers round out the picture — the people who translate between core research and product, run experiments at scale, and maintain the infrastructure that makes large training runs possible.

How to Choose the Right Firm

Most of the work in hiring a recruiting firm happens before you sign the engagement letter. A few questions separate firms that will deliver from firms that will burn your retainer.

Ask for placements in the exact role. Not "AI hires" — the specific role. If you are hiring a post-training LLM researcher, ask for the last three post-training hires the firm has placed. Vague answers mean the firm is guessing.

Meet the partner who will actually run the search. Many firms sell with their senior partner and staff with associates. Confirm in writing which person will be reading your resumes and making the calls.

Understand their sourcing model. Some firms rely almost entirely on their Rolodex. Others combine relationships with modern sourcing tools and technical research. The best firms do both. When interviewing a firm, ask how they would find candidates for your role outside of their existing network — the answer tells you whether they will still deliver after the easy calls run out. Our guide to sourcing tools for niche tech stacks covers the same research methods the best firms use internally.

Consider blending retained search with direct sourcing. Many in-house recruiting teams now run a retained firm in parallel with their own GitHub-based research, especially for individual contributor hires. The firm works the warm network, the in-house team works the publishing community, and whichever path closes the candidate wins. If you want to build that muscle on your own team, start with our guide to hiring engineers from GitHub.

Frequently Asked Questions

What is considered deep tech recruiting?

Deep tech recruiting focuses on roles that require advanced scientific or engineering backgrounds — typically AI and machine learning, quantum computing, biotech, robotics, advanced materials, and semiconductors. Candidates often hold PhDs or have published research, and hiring managers care as much about first-principles thinking as they do about shipping code.

How much do deep tech executive search firms charge?

Retained search for deep tech leadership roles typically costs 30-35% of the candidate first year total compensation, paid in thirds across the search. For individual contributor AI research roles, contingency firms charge 20-25%. Expect a $75,000 to $150,000 fee for a senior ML engineer and $200,000+ for a VP of AI or Chief Scientist.

Do I need a specialized firm or can a generalist handle AI hiring?

For senior research scientists, founding AI engineers, or roles requiring specific model architecture experience, a specialized firm almost always outperforms a generalist. Generalists rely on keyword searches that miss the nuance between a transformer architecture researcher and an applied ML engineer. Specialists maintain relationships with the narrow community that can actually do the work.

How long does a deep tech search typically take?

A retained search for a VP of AI or head of research averages 90 to 120 days from kickoff to offer acceptance. Individual contributor searches for staff research scientists or founding ML engineers usually close in 45 to 75 days. Expect longer timelines for roles requiring niche intersections, such as quantum hardware plus cryogenic systems experience.

Can recruiting firms protect sensitive IP during a search?

Reputable firms sign NDAs before engagement and will run confidential searches that mask your company identity until candidates are qualified. For truly sensitive roles — such as defense AI or proprietary model architecture work — ask the firm about their process for vetting candidates and compartmentalizing information across their team.