Base Salary
$180k - $220k/yr
Responsibilities
- Build end-to-end discovery applications for biomarker identification, target discovery and validation, small-molecule design, and gene therapy programs.
- Develop analyses, application logic, and multi-step workflows integrating ML models, statistical methods, pathway context, simulation tools, and biological domain logic.
- Create reusable Design of Experiments frameworks and workflows for compound evaluation, program prioritization, and multimodal evidence integration.
- Implement AI systems that orchestrate and chain analyses into coherent applications used by scientists.
- Extend applications for new data modalities and analysis routines while validating scientific correctness, reproducibility, accuracy, and interpretability.
- Collaborate with ML engineering, bioinformatics, and data ingestion teams to ensure workflows use consistent data.
Requirements
- Strong experience in ML, computational biology, scientific computing, or a related field.
- Deep understanding of drug discovery and preclinical development, including target identification, target validation, hit identification, hit-to-lead, lead optimization, and IND-enabling work.
- Experience building analytical workflows or application logic for biological or scientific data.
- Familiarity with differential expression, pathway analysis, clustering, enrichment, and target scoring.
- Proficiency in Python and scientific computing libraries, with experience building multi-step workflows.
- Ability to translate scientific questions into structured, reproducible workflows for real decision-making.
- Strong communication and cross-functional collaboration skills.
- Experience building LLM-powered agents or multi-agent reasoning systems, integrating multimodal biological data, using computational chemistry tools such as docking or ADMET modeling, or working with biological ontologies, curated knowledge sources, or pathway databases is preferred.
- Prior experience in a tech bio startup, biotech R&D group, or scientific software platform is preferred.
Benefits
- Comprehensive PPO health coverage through Anthem, including medical, dental, and vision.
- 401(k) with top-tier plans.
- High ownership and impact within a team of engineers, scientists, and builders.
- In-person role based in the company’s San Francisco office.
- The team ships twice weekly and iterates based on user feedback.
Tech Stack
Categories
About Mithrl
The scientific decision engine for R&D. Raw data → defensible decisions, faster. Pharma and biotech R&D often spend weeks, or even months, learning, coding, and troubleshooting discovery analysis pipelines. This slows research and diverts focus from the work that truly matters: discovery. Mithrl changes that. Using only natural language from the user, Mithrl AI builds custom workflows with full transparency and reproducibility for discovery data (omics and beyond), on-demand and in minutes, not weeks. Discovery researchers across therapeutic areas can instantly align, analyze, interpret, integrate, and iterate hypotheses for complex datasets without manual coding or repetitive setup. By automating these time-consuming processes, Mithrl empowers scientists to spend more time designing experiments, testing insights, and driving high-quality discoveries, including novel IP. From accelerating data analysis to uncovering novel targets and biomarkers, Mithrl is the Scientific Decision Engine that turns data into actionable breakthroughs. See Mithrl in action. Book a demo and transform your research today! Website: www.mithrl.com Follow us on social media: • LinkedIn: linkedin.com/company/mithrl-ai • X: x.com/mithrl_ai