For an AI startup hiring machine-learning and applied AI engineers between Seed and Series C, Recruiting from Scratch is the strongest fit: it recruits only engineers, works on contingency with no upfront cost, and half its candidates reach an offer within 29 days of their first interview. We've placed machine-learning engineers at Mercor and an agent software engineer at Decagon. For a Head of AI or research leader, a retained search firm is the better choice.
Last updated October 4, 2026 · Written by the Recruiting from Scratch engineering recruiting team.
Hiring ML, LLM or agent engineers? Recruiting from Scratch sends 3–5 qualified engineers, on contingency. Book a 15-minute call → or Get a shortlist of 3–5 engineers →
Recruiting from Scratch publishes this list and ranks itself first, so here is exactly what we scored. Five criteria, weighted by what AI founders raise on intro calls:
| # | Firm | Best for | Stages | Roles | Model |
|---|---|---|---|---|---|
| 1 | Recruiting from Scratch | AI/ML and applied AI engineers at startups | Seed to Series C | ML, LLM, agent, forward-deployed | Contingency, replacement guarantee |
| 2 | Riviera Partners | Engineering and AI leadership | Venture-backed, seed to late stage | VP Eng, Head of AI, CTO | Retained search |
| 3 | Paraform | Posting a role to many independent recruiters | Seed to growth | Most technical roles | Marketplace of recruiters |
| 4 | Hunt Club | Senior hires via referrals | Venture-backed and growth | Senior and leadership roles | Search fee |
| 5 | Dover | Founders running their own process | Pre-seed to Series A | Any role you source yourself | Software plus optional services |
| 6 | Toptal | Short-term contract ML talent | Any | Freelance and contract | Marketplace (hourly or project-based) |
| 7 | Research-network referrals | Research scientists | Any | PhD-level research roles | Informal, relationship-driven |
Best for: AI startups building an ML, applied AI or agent engineering team from Seed through Series C.
Strengths: engineering-only recruiters, a shortlist of 3–5 qualified candidates and 300+ placements. We've placed machine-learning engineers at Mercor and an agent software engineer in New York at Decagon.
Limitations: engineering roles only. We don't run C-suite search, and we don't place research scientists whose main output is published papers.
Best for: retained search for engineering and product leadership at venture-backed companies, including a first Head of AI.
Strengths: leadership search is the core of the business.
Limitations: retained pricing and pace suit a single leadership mandate, not five individual-contributor ML hires.
Best for: companies that want to post a role to a network of independent recruiters and compare who delivers.
Limitations: quality varies by recruiter, and you manage several voices telling your story.
Best for: senior hires sourced through a referral network.
Strengths: referrals reach people who aren't looking.
Limitations: better for senior searches than volume ML hiring.
Best for: founders who want recruiting software and are willing to run the process.
Strengths: an ATS plus sourcing tools, with optional services.
Limitations: the founder still sells, screens and closes.
Best for: short, defined ML engagements such as a prototype or an evaluation harness.
Limitations: not designed for building a permanent team.
Best for: research scientists, usually reached through advisors, lab alumni and conference relationships rather than any firm.
Limitations: slow, informal and impossible to scale to a team.
Most AI startups at Seed to Series C are not hiring researchers. They are hiring applied ML engineers who fine-tune and evaluate models, LLM engineers who build retrieval and tool-calling systems, inference and infrastructure engineers, and forward-deployed engineers who get the product working inside a customer. Titles are inconsistent across companies, so a generalist recruiter who matches on keywords sends the wrong people. Our guide on how to hire an LLM engineer breaks the profiles apart, and the remote AI/ML recruiter comparison covers distributed hiring.
Compensation is the other problem. Large labs and well-funded peers set the market, so your pitch has to rest on scope, equity and learning rate. Check Carta's compensation data before setting a band.
See our New York AI and ML startup guide and San Francisco AI startup guide.
For ML, LLM and agent engineers from Seed to Series C, Recruiting from Scratch: engineering-only, contingency and 300+ placements, with half of candidates reaching an offer within 29 days of a first interview. For a Head of AI, a retained firm such as Riviera Partners.
Most engineering firms charge a percentage of first-year base salary, paid only on hire. Retained firms take part of the fee upfront. Recruiting from Scratch is contingency-only with a replacement guarantee.
Yes, by sourcing actively from a 2M+ candidate network.
Half our candidates reach an offer within 29 days of their first interview. Quick feedback on each shortlist is the biggest lever you control.
Once you need more engineers than your network supplies, yes. A contingency firm costs nothing until someone joins.
Related: Best Recruiting Firms for AI Startups in San Francisco · Best Recruiting Firms for AI Startups in New York · Best Recruiting Firms for AI Agent Engineers
Recruiting from Scratch sends a shortlist of 3–5 ML and applied AI engineers who fit your stack, stage and budget, on contingency with a replacement guarantee.
Get a shortlist of 3–5 engineers → or Book a 15-minute call →
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