Research Engineer: Machine Learning and Knowledge Representation for Biodata
Core
Design and implement AI research prototypes for life sciences, focusing on genomic medicine, computational chemistry, and multi-omics data analysis to support drug discovery and molecular generation.
Role type
Research Engineer (Machine Learning & Knowledge Representation)
Builds
AI research prototypes for de novo molecule generation, property prediction, and multi-omics insights.
Domain
Life Sciences / Bioinformatics / Computational Chemistry / Genomics
Deliverable
production ML models
Required skills
Machine Learning, Deep Learning, Python, PyTorch, Parallel Computing, GPU Acceleration, Data Engineering, SQL, LLMs (RAG, PEFT, Agentic Workflows), Linux, Git
Preferred skills
Knowledge Graph technologies (RDF, SPARQL), Workflow Orchestration (Prefect), Genomic data formats (FASTQ, VCF), EHR processing, Back-end development (Django, Flask), Open-source contributions
Technologies
PyTorch, NumPy, Linux, SQL, Prefect, Django, Flask, RDF, SPARQL
Responsibilities
Design and implement AI research prototypes for life sciences; Develop and implement machine learning models for de novo molecule generation and property prediction; Design and optimize deep learning architectures for molecular generation; Build data ingestion pipelines for large-scale biochemical and multi-omics datasets; Collaborate with subject matter experts to integrate domain knowledge into AI models.
Seniority
Mid-Senior, hands-on IC
