18 Months, 40 Companies: How One Engineer Landed at Mistral
28 Jul 2026
The long road to a research engineer seat at a billion-dollar AI lab
Breaking into a research engineer role at a top-funded AI lab may take far longer than founders and job-seekers expect. One engineer's documented path to a research engineer position at Mistral — one of the few ML foundation model labs with more than a billion dollars in funding — spanned roughly 18 months, involved 40 different companies, and included a rejection after six full interview rounds with another firm.
The timeline
- ~1 year prior: The author was working in their first ML position.
- April 2024: Decided to step up career ambitions.
- August 2024: Sent the first job application.
- November 2024: Rejected after six rounds with a company (not named in the source material).
- January 2025: Began applying full-time.
- Until mid-May 2025: Spent time at Recurse Center.
- June 2025: Shifted to a more tactical, focused application strategy.
- Mid-August 2025: Received a verbal offer from Mistral.
- Early September 2025: Formally signed with Mistral.
By the numbers
- 18 months — total duration of the job search
- 40 — different companies contacted
- 60 — touchpoints across the search
- 15 — pull requests contributed to highly scrutinized open source codebases
What stands out
Two details from the timeline may be worth founders' attention. First, the process wasn't linear — a six-round rejection came fairly late, in November 2024, suggesting that even candidates who progress far into a hiring pipeline can still fail to land an offer. Second, the eventual success followed a shift in strategy: the author moved from sporadic applications to a full-time, tactical push starting in January 2025, then further sharpened efforts by June 2025 — just two months before receiving a verbal offer.
The author also contributed 15 pull requests to highly scrutinized open source codebases during this period, which may have served as a visible signal of technical capability to hiring teams, though the report does not detail the specific content of those contributions or how directly they factored into Mistral's decision.
Why founders should care
For founders and hiring managers building AI teams, this single case suggests — though it cannot confirm at scale — that:
- Hiring timelines for research engineer roles at well-funded foundation model labs may commonly stretch well beyond a few months, meaning both candidates and companies should likely plan for extended search or hiring cycles.
- Broad outreach (40 companies, 60 touchpoints) could be a realistic baseline for candidates targeting competitive AI research positions, which may inform how founders think about candidate pipelines and applicant expectations.
- Visible technical contributions, like open source pull requests to scrutinized codebases, may plausibly influence how technical hiring teams evaluate candidates, even if the exact weight given to such work is unclear.
- A shift from casual to full-time, structured job-search effort appears associated with improved outcomes in this case, suggesting founders evaluating their own hiring funnels might consider whether structured, tactical approaches yield better results than ad hoc ones.
What's missing
The report leaves several gaps: the company behind the November 2024 six-round rejection is not identified, Mistral's own interview process is not described, and details about the author's prior ML role, compensation, or specific role scope at Mistral are absent. The content of the 15 open source pull requests is also not specified. These gaps limit how far the lessons here can be generalized, but the timeline itself offers a useful, if singular, data point for anyone trying to gauge the realistic effort required to land a research engineer role at a leading AI lab.