Member of Technical Staff, Discovery Applications

ABOUT MITHRL

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.

ABOUT THE ROLE

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

WHAT YOU BRING

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

Nice to Have

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

WHAT YOU WILL LOVE AT MITHRL

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.