Tempus
Precision medicine platform combining clinical, molecular, and imaging data with AI.
Typical Backgrounds
Types of experience professionals commonly develop
Roles Companies Often Recruit
Common roles when hiring Tempus-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
Tempus and the precision medicine data-platform talent market
Tempus built its business by combining clinical, molecular, and imaging data with machine learning to support precision oncology decisions, and its 2024 IPO put it firmly on the map as a public techbio. That combination of healthcare data infrastructure and applied AI makes Tempus a genuine crossroads company: half of its hiring looks like a health tech data company and half looks like a molecular diagnostics lab.
Recruits Lab sees the strongest demand for Tempus alumni in machine learning applied to clinical and genomic data, and in clinical data platform engineering, since both roles require the specific combination of healthcare domain knowledge and modern ML/data infrastructure skill that pure tech companies and pure biotechs each lack half of.
Org structure and titles that matter
Tempus organizes around Data Science/Machine Learning, Genomics/Bioinformatics, Clinical Data Platforms Engineering, Regulatory Affairs, and Commercial (oncology and, increasingly, other therapeutic areas). Titles to know: Director and Senior Director of Machine Learning, Director of Bioinformatics, Director of Clinical Data Platforms, and Director of Regulatory Affairs for its expanding suite of lab-developed tests and companion diagnostics.
Tempus's commercial organization sells into oncology practices and increasingly into pharma partnerships for real-world data licensing, so its Commercial titles include both traditional diagnostics sales and business-development roles focused on pharma data partnerships, a distinctly Tempus-shaped hybrid.
Hiring bar and interview process
Machine learning and data science interviews at Tempus include a technical coding assessment plus a case study on applying ML to a clinical or genomic dataset, testing both engineering rigor and domain judgment about clinical validity. The company has a reputation for moving fast in interviews relative to traditional biopharma, often compressing the loop into two to three weeks.
Clinical and regulatory interviews focus on lab-developed test and companion diagnostic pathway experience, while commercial interviews for the pharma-data-partnership roles assess comfort navigating both a diagnostics sale and a data-licensing business development conversation simultaneously.
Compensation posture
Tempus's 2024 IPO changed its equity story from pre-IPO options to publicly traded RSUs, which recruiters should note when comparing offers: candidates now weigh liquid, valued equity rather than speculative pre-IPO shares, closer to a large-cap comp conversation than a typical growth-stage biotech pitch.
| Level / Function | Typical Total Comp Range | Equity Form | Notes |
|---|---|---|---|
| Senior Director, Machine Learning | $230K–$300K | Tempus RSUs post-IPO | Cash-plus-equity mix shifted after 2024 IPO |
| Director, Bioinformatics | $200K–$260K | Tempus RSUs | Genomics/data hybrid skill set |
| Director, Clinical Data Platforms | $200K–$260K | Tempus RSUs | Health data infrastructure experience |
| Director, Regulatory Affairs | $190K–$250K | Tempus RSUs | LDT and companion diagnostic pathway experience |
| Director, Pharma Data Partnerships | $210K–$270K base + variable | Tempus RSUs | Business development skill layered on diagnostics knowledge |
Which profiles transfer well, and where
The clearest transition out of Tempus is a machine learning leader moving into a biotech's translational data science function, since Tempus alumni already understand how to make ML outputs clinically defensible, a skill that pure tech-trained ML engineers often lack.
| Origin Team | Strengths | Best-Fit Destination | Watch-Outs |
|---|---|---|---|
| Machine Learning / Data Science | Applied ML on real clinical and genomic datasets | Precision medicine techbio, healthcare AI startup, or pharma real-world-data team | Domain-specific clinical validity knowledge doesn't always transfer to non-healthcare ML roles |
| Clinical Data Platforms Engineering | Healthcare interoperability and data pipeline architecture | Health tech platform or hospital-system data infrastructure team | Compliance and privacy requirements vary significantly by company and use case |
| Regulatory Affairs, LDT/Companion Diagnostics | Multi-modal diagnostics regulatory strategy | Diagnostics or precision medicine startup building a new test | Companion diagnostic pathways are indication-specific and require re-learning per program |
| Pharma Data Partnerships | Hybrid business development and diagnostics domain expertise | Biotech or techbio building a real-world-data commercialization arm | Requires comfort with long enterprise sales cycles unlike consumer or SMB sales |
How to recruit out of Tempus
Motivators: interest in earlier-stage equity again after the IPO reset some candidates' risk appetite, and desire to work on a narrower, deeper clinical problem versus Tempus's broad multi-cancer-type platform. Blockers: genuinely interesting technical problems and a fast-paced culture that keeps engagement high, plus newly liquid RSUs that some candidates want to fully vest before leaving.
Tempus's Chicago headquarters anchors much of its engineering and data science team, though the company has hired more distributed talent since its IPO. Counter-offers tend to move quickly given the company's tech-company-speed HR processes, and often include accelerated vesting or refreshed RSU grants rather than large base salary jumps.
Frequently asked questions
How did Tempus's 2024 IPO change how recruiters should approach its talent?
Pre-IPO options became publicly traded RSUs with a real, verifiable value, shifting Tempus comp conversations closer to a large-cap tech negotiation than a speculative growth-stage biotech pitch. Recruiters should model liquid equity value explicitly when building competing offers.
Why is Tempus machine learning talent valued outside pure tech companies?
Tempus's ML teams work directly with clinical and genomic data under real regulatory and clinical-validity constraints, giving them domain judgment that pure tech-trained ML engineers typically lack. That combination is scarce and valuable to any healthcare AI or precision medicine company.
What is Tempus's interview process speed compared to traditional biopharma?
Tempus moves faster, often compressing technical and case-study interview loops into two to three weeks, reflecting its tech-company operating rhythm rather than the longer, more bureaucratic processes typical of large pharmaceutical companies.
Does Tempus's dual data-and-diagnostics business model create distinct career tracks?
Yes. Its commercial organization includes both traditional oncology diagnostics sales and a separate pharma data-partnership business development track focused on real-world-data licensing, which recruiters should treat as genuinely different skill sets rather than interchangeable sales roles.
What functions is Tempus expanding since its IPO?
Tempus continues to grow beyond oncology into other therapeutic areas, including cardiology and mental health data products, driving hiring in machine learning, bioinformatics, and therapeutic-area-specific clinical data platform roles.