AI and Machine Learning Recruiters: 22 Firms Compared

AITA's success stories are anonymous but specific. It reports placing five senior AI engineers and technical leads for a healthtech company within two weeks, 30 engineers over six months for an autonomous vehicle company and 15 AI specialists, including ML engineers, for an education technology company over three months. It also attributes later results to those teams, such as a 25% cut in system errors; those are the firm's own accounts of its clients' outcomes.

The firm calls itself "a recruiting agency exclusively focused on artificial intelligence". Its homepage (opens in a new tab) reports a 14-day average time to be introduced to the hire and 92% of engineers still in role after one year. The same 156+ figure is labeled clients served on the homepage and placements on the success stories page, and no tile states a method. Its pricing page (opens in a new tab), titled 2024 Services and Pricing, sets out the three models above; for executive search, more than half of the fee is due on success.

Harnham's talent solutions page (opens in a new tab) reports that 16,600 data and AI professionals have secured roles through it, and 1,076 data, AI and analytics professionals placed in 2025. It gives an average of 66 days to fill AI/ML positions and 10 days to fill a contract position, without saying which stage the average ends at. Its July 2025 post on reaching 15,000 placements says that history, which began in 2006, runs from analysts to chief data officers, AI directors and heads of AI. None of these totals is an AI-only count.

Its AI and machine learning page lists AI architecture, AI engineering, generative AI, applied data science, ML engineering, MLOps, ML platform engineering and natural language processing roles, with permanent, contract and executive search. The talent solutions page describes an "exclusive focus on data and AI roles" and carries named testimonials; Johnson Kuan of DirecTV credits Harnham with "finding top Data Science & Engineering talent". Harnham also supplies contract data consultants and training, so ask which service a proposal covers.

Magna presents itself as "Specialist Recruiters In Artificial Intelligence" and describes its clients as working in AI, IT engineering, machine learning, autonomous voice assistants and electric vehicles. The roles it was advertising when read, all in London, included an AI/ML engineer and a robotics scientist.

Its pages name no client, placement or case, and they publish no fees or guarantee. Its About page (opens in a new tab) gives the team's combined experience as over 20 years. Ask for references from AI hires before you engage it.

RWA publishes named testimonials from AI companies. Jacob Hurst, founder and CTO of Etcembly, thanks it for its "diligent way of working on this AI Engineer hire", and Tim Closs, CTO of msk.ai, describes a senior, niche role filled through Richard Wheeler with hand-picked candidates.

The firm describes itself as a specialist AI, machine learning and data science recruitment consultancy, recruiting for scale-ups and spin-outs from Oxford, Cambridge and UCL. Its FAQs add that it also fills software engineering roles, such as Python, Java and cloud developers. It aims for one offer for every two shortlisted CVs, and its FAQs put the time to a screened shortlist at usually between two days and two weeks. Richard Wheeler founded the firm in 1987 as a technology recruiter, according to its About page (opens in a new tab); the pages read do not say when its AI focus began.

Adapt Talent's case studies include Securiti.ai, where it says it sourced machine learning, DevSecOps and compliance experts for an expansion into EMEA and APAC, and Polycam, where it filled computer vision, AR/VR and full-stack product roles; a third names Gensyn.ai's ML infrastructure team without describing the roles. Three anonymous AI case studies describe searches for an autonomous vehicle and physical AI company, a multimodal AI platform and a robotics foundation model team. No figures are given, and the page's structured data dates it to 2025.

Its AI page lists computer vision, deep learning, NLP, speech recognition, MLOps, large language models, ML infrastructure, generative AI and AI agents, and says it reaches candidates through conferences such as NeurIPS, CES and CVPR. Its services page (opens in a new tab) offers contingent, contract, retained and RPO recruiting; a contingent fee is due once the hire starts.

The figures Alpha Apex Group publishes are firm-wide: 2,000+ placements worldwide, a 43-day average time-to-fill for full-time hires, and 72 hours until new candidates reach the hiring manager, with no stated scope. The client logos on its pages, including Weights & Biases, AWS and Meta, do not say what work was done. The pages read publish no AI case study, fee or guarantee, so ask for examples of AI searches it has completed.

Its AI page offers AI recruitment and executive search, with an assessment of technical, strategic and leadership capability against the role. A separate machine learning page covers ML leadership searches and planning for new or growing ML teams.

CalTek's machine learning page describes an international client whose internal recruiters could not find the skills it needed; within two weeks of being contacted, CalTek says it produced the machine learning engineers and data scientist the client's own recruiters had not found. The client is unnamed, and the page was first published in 2021 and updated in September 2025.

CalTek recruits ML engineers, data scientists, NLP engineers and computer vision engineers on temporary, temp-to-permanent and direct-hire terms. Asked about one-year retention for machine learning hires, the page answers 91% for permanent placements, with temporary placements lower. It describes a standard process that typically spans one week and says clients pay through "diverse fee structures", without rates. Its About page (opens in a new tab) dates the firm to 2002.

Focus GTS publishes client logos on its AI staffing page, including Adobe, American Express, Sony and Verizon, and says it has placed "hundreds of engineers". It says it actively places agentic AI engineers and LLM application developers for enterprise teams, without naming those clients or reporting results, and it does not say which logos were AI work. Its About page (opens in a new tab) centers on Adobe Experience Cloud staffing, and its AI page also covers specialists for Adobe, Salesforce, Databricks and Snowflake AI products.

It offers contract, contract-to-hire, full-time and executive search for AI, ML and data roles, and says candidates are screened by people who have shipped production AI, with calls to prior managers. Its timelines are specific: contract placements for senior ML engineers often take 2 to 4 weeks when the client's process is decisive, and full-time AI architects 6 to 12 weeks. Its downloadable AI hiring playbook includes a one-page brief covering the business outcome and what must be working by day 90.

Groom reports 450+ AI professionals placed and 250+ companies served across Canada and the US on its AI page. The logos there include Mila, Paladin AI, Ruths AI and Google, and the page quotes Jean-François Isabelle of Résolution, who says Groom helped grow its data science and business analysis teams.

The page names its AI division lead, a director of technology who has placed AI, data and engineering professionals since 2014. Groom offers permanent, contract and executive hires, and its three-step process runs from a conversation about what success looks like to involvement through offers and onboarding. The larger figures on its About page (opens in a new tab) are firm-wide.

Kaliba recruits AI research scientists, AI engineers, AI architects, ML engineers, data scientists and data analysts, on permanent and contract terms. It also places technology sales professionals with AI and ML experience, which suits a vendor building a sales team for an AI product.

The pages read name no AI client, placement or case, and publish no fee or guarantee. Its talent solutions page (opens in a new tab) describes an executive search team for technology vendors in the region.

The recruitment page lists roles the firm has filled, among them Manager, AI/ML; Machine Learning Software Engineer; Manager, Data Science; and Lead Research Engineer/Scientist. It names no clients or dates for those roles.

The page says the firm often pairs engineering talent in AI, machine learning and data science with startups, and also recruits leadership, operations, sales, marketing and HR roles. Its search framework covers intake, outreach, interview preparation, debriefing and offers. If a hire does not fit, it offers a three-month free replacement guarantee.

Madison Bridge's clients page (opens in a new tab) carries testimonials from technology employers, including Workday and Sommet Education; one talent acquisition manager reports more than 20 positions filled. None mentions an AI hire, and the page was last updated in 2022.

Madison Bridge recruits AI researchers, AI engineers, ML engineers and ML researchers, on permanent, contract and interim, executive search and retained, and recruitment as a service (RaaS) models.

OnHires publishes named case studies, several tagged AI and ML: a senior AI engineer for Epicbrief, a senior machine learning engineer for Data Monsters, an AI engineering role for Sybal Corp and a machine learning role for Rainbow Weather.

Its AI page reports four weeks on average to close a vacancy, while its FAQ says its recruiters spend three weeks on average to complete an order; ask which applies to your role. The page also reports three technical interviews with candidates per week. The same page's FAQ says OnHires "is not a data science recruiting agency or an AI recruitment agency" and searches across a wider range of IT talent. Its company page (opens in a new tab) gives a 32% average cost saving with no baseline.

PALTRON's homepage (opens in a new tab) carries named testimonials, such as one from ITA Shipping on a CTO search for LetMeShip, and client logos including Microsoft and IBM. None describes an AI hire.

PALTRON recruits ML engineers, deep learning engineers, MLOps engineers, generative AI specialists and AI product managers, and its AI page promises suitable candidates "all within just 30 days". Its homepage describes a selection phase lasting 2 to 4 weeks.

Razoroo's homepage (opens in a new tab) reports 125+ technical placements in three years and quotes Aika Zhang, VP of talent at Silverline tech, on fast recruiting and "technical acumen". The homepage does not identify these as AI results, and the site's solutions menu lists technology sales, UX and blockchain recruiting alongside AI and ML.

Its AI and machine learning page describes "a pure machine learning and artificial intelligence recruiting firm" and an evaluation of coding ability, algorithmic thinking and system design through custom challenges and live interviews. The same page calls Razoroo the top AI recruiting company both "3 Years in a row" and "five years in a row", without naming a ranking.

A testimonial on Redfish's machine learning page from Pilot AI Labs, attributed to its director of finance, says Redfish helped it hire three PhD-educated candidates. The same page reports placing a chief product officer with industrial AI experience at a venture-backed startup within three weeks. Its AI page describes further anonymous placements with times to fill: a full-stack engineer for an industrial AI company found within 11 days, a senior product manager for a machine learning software company in 12 days, and a deep learning engineer and senior architect for a biotech startup working on generative AI for drug discovery.

The firm was founded in 1996. It says it takes 4 weeks on average to fill an AI or ML role, and that its engaged search normally runs four weeks from discovery call to offer, with at least five resumes by the end of the first week. Its 86% retention rate covers all the sectors it recruits for.

Stott and May's Summation case study reports four senior AI software engineers and an AI product manager hired for the Bellevue, Washington company ahead of its Series A, with a 100% fill rate and five weeks' average time-to-hire, in a relationship that began in 2024. CEO Ian Wong calls the firm "a game-changer for our engineering hiring". Its AI page describes a second client, Fleet, where it reports four researcher placements in New York.

The AI and machine learning page reports a four-week average time-to-hire and a 93% offer acceptance rate across all search assignments over a 12-month period, and says 83% of placed candidates were not actively looking. It gives its fastest AI engineer hire as 9 days in one place and 12 days in another.

Alliance's AI page quotes Robert Williams, CTO of an unnamed AI and technology company, who says the candidates had the technical experience the company was looking for. Its case-study index shows no AI or ML engagement on its first page.

The page separates two services: recruiting ML engineers, AI engineers, data scientists, research scientists, MLOps, computer vision and NLP engineers and AI product managers, and using AI tools in its own search. It says no candidate is rejected by an algorithm without human review. It promises a shortlist within 24 to 48 hours of an agreed brief, though its numbered process puts shortlist delivery on day 5, and it puts senior appointments at 4 to 8 weeks. A pop-up on the page offers a 90-day replacement guarantee. The page gives both 5,000+ and 50+ top-level positions without reconciling them, so ask what they count.

Insight Global's case study, posted in September 2026, describes building a technology team in Costa Rica for an unnamed infrastructure engineering company, including AI professionals, software developers and DevOps engineers. It reports 14 consultants placed between February and August 2026, with every opening filled in about two to three weeks.

Its AI page combines staffing with advisory and managed AI services, and lists roles from AI and machine learning engineers to MLOps engineers, GenAI specialists and forward deployed engineers. Its AI engineer page (opens in a new tab) says it typically identifies and screens candidates in 24 to 48 hours, with 1 to 3 weeks the typical time from interviews to the person working. Its scale figures, such as 50,000+ consultants placed a year, are firm-wide.

TCM's testimonials page (opens in a new tab) is anonymous; one, from an MSP program manager at a financial services and insurance client, says TCM filled six positions including a data scientist. No other AI or ML hire is described.

Its AI staffing page lists ML engineers, software engineers, data scientists, data engineers, architects and product managers, and describes AI-assisted matching in its own process. TCM offers staff augmentation, contract-to-hire, direct hire and SOW-based teams. Its why work with us page (opens in a new tab) reports over 63,000 consultants and core employees deployed since 1980, firm-wide, and guarantees client satisfaction without stating a period.

Nexus's case studies include Lavorro, which builds AI and machine learning software for chip fabs: Nexus filled a VP of global sales and a director of application engineering on retained search. Lavorro's COO, Akshay Oberoi, says Nexus brought two strong people out of Applied Materials. Both were leadership hires at an AI software company; the case describes no ML engineering placement.

The AI and machine learning page covers accelerator design, NPU and GPU architecture, ML compilers, applied ML and AI infrastructure roles. Nexus places permanent, direct hires only, and its recruitment solutions page (opens in a new tab) says most searches reach a shortlist within one to two weeks of kickoff, with an offer around four to six weeks.

Radley James's homepage reports 2,000+ placements since 2009 and 94% still in seat after a year, both firm-wide. It places quant researchers, engineers, ML specialists and trading technologists with hedge funds, proprietary trading firms and fintechs, and lists Machine Learning & Data Science among its nine markets.

Its three case-study titles do not mention machine learning, and the pages read publish no fees or guarantee. For broader finance hiring, see the hedge fund recruiters guide.