What are the responsibilities and job description for the Senior Scientist, AI/ML Drug Discovery position at HireMinds?
Senior Scientist, AI / ML Drug Discovery
Stealth-Mode Biotech | SF Bay Area, CA
title and compensation flexible based on background / pedigree
Our client is a stealth-mode biotech startup pioneering AI-first approaches to drug discovery, integrating deep learning, generative models, and large-scale biological datasets to accelerate the development of first-in-class therapeutics.
Responsibilities
Design and implement novel generative architectures (e.g., VAEs, GANs, Diffusion Models) for in silico drug design using chemical space representations such as SMILES, SELFIES, and molecular graphs.
Apply Graph Neural Networks (GNNs) and Transformer-based architectures to learn molecular embeddings, model protein-drug interactions, and predict functional outcomes.
Leverage multi-omics datasets (RNA-seq, proteomics, single-cell transcriptomics, epigenomics) to extract biologically meaningful insights and integrate them into predictive models.
Optimize large-scale deep learning pipelines for sequence-based and structure-based drug discovery applications, including protein folding (AlphaFold-inspired), docking simulations, and binding affinity predictions.
Contribute to the development of scalable, cloud-based ML workflows (AWS / GCP, Kubernetes, Ray) for high-throughput model training and inference.
Maintain cutting-edge knowledge of AI / ML in drug discovery and integrate best practices into our workflows.
Qualifications
PhD (or MS with equivalent experience) in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Computational Chemistry, or a related field.
3 years of hands-on experience in applying deep learning to drug discovery, structural biology, cheminformatics, or genomics.
Proven expertise in deep learning architectures, including Transformers (BERT, ESM, AlphaFold), GNNs (GraphConv, GAT, SchNet, GVP), VAEs, and Diffusion Models.
Strong background in molecular representation learning, including SMILES embeddings, graph-based molecular descriptors, and structure-based protein-drug modeling.
Experience with multi-modal AI models that integrate chemical, biological, and clinical datasets for predictive analytics.
Ability to handle high-throughput biological data processing, including working with sequencing data, expression matrices, and biomarker discovery pipelines.
Strong software engineering skills in Python, NumPy, Pandas, SQL, and distributed computing (Dask, Ray, Spark).
Experience deploying scalable AI models in cloud environments (AWS SageMaker, Vertex AI, Kubernetes, Docker).
A strong publication record in machine learning, computational biology, or AI-driven drug discovery.
Why Join?
Stealth-mode opportunity – Influence foundational AI-driven drug discovery pipelines before public launch.
Cutting-edge AI / ML research – Work with top experts in AI and computational biology.
Massive impact – Accelerate drug development for diseases with high unmet need.
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