Recursion
Techbio company applying machine learning to industrialized drug discovery.
Typical Backgrounds
Types of experience professionals commonly develop
Roles Companies Often Recruit
Common roles when hiring Recursion-caliber talent
- Principal Scientists
- Clinical Development Leaders
- Regulatory Affairs
- Medical Science Liaisons
- Bioinformatics Scientists
- Executive Leadership
Related Resources
Salary guides, hiring guides, market reports and case studies
Recursion and the techbio drug-discovery talent market
Recursion built one of the industry's largest experimental datasets pairing automated cell biology with machine learning, positioning itself as a leading example of the 'techbio' model that aims to industrialize early drug discovery. Its merger with Exscientia further consolidated it as a platform company blending automated wet-lab science, computational chemistry, and ML in a single organization.
Recruits Lab sees the strongest demand for Recursion alumni in machine learning applied to phenotypic screening and in automated cell biology/lab operations, since the specific combination of high-throughput automation and ML model development at Recursion's scale is rare outside a handful of similarly structured techbios.
Org structure and titles that matter
Recursion organizes around Machine Learning/Data Science, Automated Cell Biology (lab operations), Computational Chemistry (post-Exscientia merger), Clinical Development, and Business Development/Partnerships. Titles to know: Director and Senior Director of Machine Learning, Director of Automation/Lab Operations, Director of Computational Chemistry, and Director of Business Development for pharma partnership deals, a genuinely important function since Recursion's business model depends heavily on partnered programs alongside internal pipeline.
Recursion's Clinical Development function is smaller and newer relative to its platform organization, reflecting the company's earlier-stage position on translating platform hits into clinical candidates compared to established biopharma peers.
Hiring bar and interview process
Machine learning interviews at Recursion include a technical coding assessment and a discussion of experience with large-scale image or phenotypic data, since the platform's core dataset is built on automated cell-imaging assays. Lab operations and automation interviews assess experience with robotics and high-throughput screening workflows at industrial scale.
Business development interviews for pharma partnerships weigh deal-structuring experience and scientific credibility together, since partners are evaluating whether the BD lead can speak knowledgeably to platform capabilities during diligence.
Compensation posture
Because Recursion is publicly traded but still a smaller-cap, earlier-commercial-stage company, its RSU value is more volatile than a large pharma's, so recruiters should be candid with candidates weighing an exit about both the upside and the downside risk relative to Recursion's current share price.
| Level / Function | Typical Total Comp Range | Equity Form | Notes |
|---|---|---|---|
| Senior Director, Machine Learning | $220K–$290K | Recursion RSUs, publicly traded | Equity value tied to a more volatile small-cap stock |
| Director, Automation / Lab Operations | $190K–$250K | Recursion RSUs | Industrial-scale robotics screening experience |
| Director, Computational Chemistry | $210K–$270K | Recursion RSUs | Post-Exscientia merger function |
| Director, Business Development (Pharma Partnerships) | $200K–$260K base + variable | Recursion RSUs | Deal-structuring and platform-credibility skill combo |
| Director, Clinical Development | $220K–$280K | Recursion RSUs | Smaller, newer function relative to platform organization |
Which profiles transfer well, and where
A common transition out of Recursion is a Director of Machine Learning moving to a pharma's internal AI drug discovery group, trading platform-company equity risk for large-pharma stability while bringing genuinely differentiated phenotypic-screening ML experience that most pharma AI teams lack.
| Origin Team | Strengths | Best-Fit Destination | Watch-Outs |
|---|---|---|---|
| Machine Learning / Phenotypic Screening | Large-scale image-based ML model development for biology | Any techbio or pharma building an internal ML-driven discovery platform | Recursion's dataset scale is hard to replicate; expect a smaller-data adjustment period |
| Automated Cell Biology / Lab Operations | Industrial high-throughput screening and robotics workflow design | Biotech building or scaling its own automated lab | Capital intensity of automation infrastructure varies widely by company stage |
| Computational Chemistry | AI-assisted molecule design post-Exscientia integration | Small-molecule biotech investing in computational drug design | Integration-era tooling may differ from a standalone chemistry-first company's stack |
| Business Development, Pharma Partnerships | Platform-deal structuring with major pharma partners | Techbio or platform biotech scaling its partnership strategy | Requires genuine scientific fluency, not just generic BD skills |
How to recruit out of Recursion
Motivators: desire for equity stability after share price volatility, and interest in applying platform skills to a company with a more advanced clinical pipeline. Blockers: genuine excitement about the scale of Recursion's proprietary dataset, which is difficult to replicate elsewhere and keeps technical talent engaged despite stock volatility.
Recursion's Salt Lake City headquarters is a less crowded talent market than Boston or the Bay Area, meaning remote flexibility is a real consideration for both attracting and losing candidates. Counter-offers tend to focus on refreshed equity grants at current share prices and continued access to Recursion's dataset and compute infrastructure, framed as things a competing offer often cannot match.
Frequently asked questions
Why is Recursion's machine learning talent attractive to pharma AI teams?
Recursion has built one of the largest proprietary phenotypic screening datasets in the industry, giving its ML scientists hands-on experience training models on massive, real biological image data. That specific data-scale experience is hard to find inside a traditional pharma's smaller internal AI teams.
How did the Exscientia merger change Recursion's talent profile?
It added a substantial computational chemistry organization to Recursion's existing cell biology and ML strengths, creating combined AI-drug-design capability across both biology and chemistry that neither company had alone before the merger.
Is Recursion equity considered high-risk for candidates evaluating an offer?
It is more volatile than large pharma equity given Recursion's smaller market cap and earlier commercial stage, so recruiters both hiring into and out of Recursion should model realistic upside and downside scenarios rather than treat RSUs as a fixed-value component.
What does Recursion's business development role for pharma partnerships require?
It requires genuine scientific credibility to speak knowledgeably about platform capabilities during partner diligence, combined with deal-structuring experience, since Recursion's business model depends significantly on partnered programs with major pharma companies.
What functions is Recursion currently expanding?
Recursion continues to invest in computational chemistry integration following the Exscientia merger, along with clinical development capacity as more platform-derived programs advance into human trials.