ML Engineer, Discovery Applications
MithrlGenomic Data company
San Francisco, United StatesMid
Headline
Bonfire Ventures
Jenny Fielding
Scott Hartley
Agi House
Obvious Ventures
Data & AI
About the role
TL;DR
Build scientific workflows for drug discovery and biomarker identification.
- •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.
- •Key Responsibilities Build full discovery applications that support biomarker identification, target discovery, target validation, small molecule design workflows, and gene therapy programs 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 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 Requirements 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
Required skills
Pythonscikit-learnTensorFlowPandasNumPyMLflow
Nice-to-have skills
Hugging FacePyTorchKubeflow
Domain expertise
biotech
Tech stack
PythonPandasNumPyscikit-learnTensorFlowPyTorchHugging FaceMLflowKubeflowPostgreSQLAWS