ML Engineer, Discovery

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.

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 , 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.

• 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 multimodal 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 , 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.

Nice to Have

• Experience building LLM powered agents or multi‑agent reasoning systems.
• Experience with multimodal 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.
• Benefits:
  Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top‑tier plans.