Formation Bio
Tech and AI-driven pharma accelerating drug development
Machine learning & AI jobs at Formation Bio
Formation Bio has 5 open machine learning, AI and data science roles on CompBioJobs. Across 11 of its AI/ML postings that state pay, the typical posted range is $186K–$267K.
- Data Scientist, Medical Imaging (Open Level) · Boston, MA; New York, NY
- Data Scientist (Open Level) · Boston, Massachusetts
- Senior / Principal Data Scientist, EHR AI · New York, NY; Boston, MA; San Francisco, CA; Raleigh-Durham, NC
- Senior Data Scientist - Real World Data · Boston, Massachusetts
- Data Scientist · Boston, Massachusetts
About Formation Bio
Funding & Growth
✓ Pros
- • Long-term well-funded runway
- • Genuine influence on which drugs get developed, not just analysis
- • Hybrid 3-days-in-office (NYC + Boston) with some flexibility
- • Cross-functional exposure (clinical, BD, commercial)
✗ Cons
- • Hybrid in-office requirement
- • Pharma-velocity, not consumer-tech-velocity timelines
- • Drug development outcomes are not under any one team's control
🏢 Working Here
Formation Bio (founded 2016 as TrialSpark) is a tech-and-AI-driven pharmaceutical company that in-licenses or acquires drug candidates and accelerates their development through clinical trials using internal technology platforms.
The company has a notable culture of cross-functional collaboration between data scientists, clinical operations, and drug development teams — bioinformatics and data science work directly influences which assets the company pursues and how trials are designed.
The funding backdrop (a16z, Sequoia, Sanofi, Thrive) gives the team long-horizon runway to invest in foundational infrastructure rather than only short-term deliverables.
🧬 Bioinformatics Focus
Formation Bio's data science work spans real-world data (RWD) infrastructure for drug acquisition decisions, AI/ML model development for clinical trial feasibility and patient stratification, multi-omics integration where assets warrant it, medical imaging models for endpoints, and broader use of LLMs and generative AI for the drug development workflow.
The team works heavily with EHR, claims, registry data, and OMOP-standardized cohorts.
Common technical requirements include Python, SQL, cloud (AWS), and exposure to clinical or biomedical data.