Base Salary
$180k - $220k/yr
Responsibilities
- Build AI-driven analysis agents for biological reasoning across diverse datasets.
- Develop reusable analysis modules for differential expression, pathway analysis, feature importance, clustering, scoring, enrichment, and mechanism-of-action interpretation.
- Create multi-step workflows combining ML models, statistical logic, and biological knowledge.
- Design agentic reasoning strategies that run and synthesize multiple analyses into coherent scientific narratives.
- Integrate simulation and modeling tools for small-molecule drug discovery, including ADMET prediction, docking scoring, generative chemistry, and structure-based modeling.
- Collaborate with data engineering, bioinformatics, and curation teams on clean and consistent data.
- Validate results, benchmark pipelines, and ensure scientific accuracy and reproducibility.
- Contribute to the architecture for AI reasoning, hypothesis testing, and simulation.
Requirements
- Strong experience in machine learning, computational biology, or a related scientific ML field.
- Experience developing analysis modules for biological or scientific datasets.
- Familiarity with target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling.
- Hands-on experience with computational chemistry or simulation tools such as ADMET models, docking, binding prediction, or molecular generative models.
- Proficiency in Python and scientific computing libraries.
- Experience designing multi-step reasoning or workflow-based ML pipelines.
- Ability to translate messy scientific questions into structured ML or analytical workflows.
- Strong communication skills and comfort collaborating with cross-functional scientific and engineering teams.
- Experience with LLM-powered scientific agents or multi-agent architectures is a plus.
- Familiarity with phenotype-based discovery, multimodal integration, systems biology, biological ontologies, curated knowledge graphs, or pathway databases is a plus.
- Background in computational chemistry, structure-based drug discovery, biotech R&D, or scientific platform teams is a plus.
Benefits
- Comprehensive PPO health coverage through Anthem, including medical, dental, and vision.
- 401(k) with top-tier plans.
- High-ownership role with direct impact on drug-discovery programs.
- In-person position based in Mithrl’s San Francisco office.
- The team ships twice per week 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