ML Engineer, Discovery Applications
5 days ago
We imagine a world where new medicines reach patients in months, not years, and where scientific breakthroughs happen at the speed of thought.
Mithrl is building the world's first commercially available AI Co-Scientist. It is a discovery engine that transforms messy biological data into insights in minutes. Scientists ask questions in natural language, and Mithrl responds with analysis, novel targets, hypotheses, and patent-ready reports.
Our traction speaks for itself:
- 12X year-over-year revenue growth
- Trusted by leading biotechs and big pharma across three continents
- Driving real breakthroughs from target discovery to patient outcomes.
We are hiring an ML Engineer, Discovery Applications to build the high level, end-to-end scientific workflows that power real bench to bench decision making inside the Mithrl platform. This role focuses on building the application layer on top of the AI Co-Scientist. Your work will shape how scientists discover biomarkers, identify and validate targets, design experiments, and run early discovery programs that extend all the way to IND-enabling work.
This role requires a deep understanding of the discovery and preclinical development cycle. You should understand how research teams move from early target hypotheses to biomarker strategy, hit identification, hit to lead, lead optimization, and preclinical validation. Your applications will support decision making across this entire arc and will be consumed directly by scientists and program teams.
You will design multi step workflows that combine analysis modules, ML models, domain logic, and agentic reasoning into complete applications. These applications cover biomarker discovery, target ID, target validation, small molecule hit identification and optimization, and gene therapy workflows. You will also extend applications to support new data modalities as our platform expands.
WHAT YOU WILL DO
- Build full discovery applications that support biomarker identification, target discovery, target validation, small molecule design workflows, and gene therapy programs
- Stand up new analyses that support application logic and improve or extend the existing analysis suite
- Create multi step reasoning flows that integrate ML models, statistical methods, pathway context, simulation tools, and biological domain logic
- Design application specific workflows for compound evaluation, program prioritization, and multi modal evidence integration
- Extend existing applications to incorporate new data modalities and new analysis routines
- Build reusable frameworks for Design of Experiments across biomarker discovery, target ID, validation, small molecule programs, and gene therapy
- Implement and improve the AI systems that orchestrate and chain analyses into coherent applications used directly by scientists
- Collaborate closely with ML engineers, bioinformatics teams, and data ingestion teams to ensure workflows run on consistent data
- Validate scientific correctness and ensure applications produce accurate, reproducible, and interpretable results
Required Qualifications
- Strong experience in ML, computational biology, scientific computing, or a related field
- Deep understanding of the drug discovery and preclinical development cycle including early discovery, 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 key discovery analysis methods such as differential expression, pathway analysis, clustering, enrichment, and target scoring
- Proficiency in Python and scientific computing libraries and comfort with building multi step workflows
- Ability to convert scientific questions into structured, reproducible workflows that support real decision making
- Strong communication skills and ability to collaborate with cross functional engineering and biology teams
- Experience building LLM powered agents or multi agent reasoning systems
- Experience with multi modal biological data integration
- Experience with computational chemistry tools such as docking or ADMET modeling
- Familiarity with biological ontologies, curated knowledge sources, or pathway databases
- Prior experience in a tech bio startup, biotech R&D group, or scientific software platform
- High ownership and impact: You will build the decision making applications that scientists rely on throughout the discovery and preclinical process
- Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders
- Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution
- Speed: We ship fast (2x/week) and improve continuously based on real user feedback
- Location: Beautiful SF office with a high-energy, in-person culture
- Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
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