Senior Machine Learning Scientist (Multimodal Drug Discovery)
Senior Machine Learning Scientist (Multimodal Drug Discovery)
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Salary Not Disclosed
1 Vacancy
Job Description
Coders Connect is collaborating with a cutting-edge biotech startup redefining how foundational biology meets AI. Their mission Train multimodal foundation models from real human biological data to revolutionize drug discovery.
What You ll Be Doing:
We re seeking a Senior Machine Learning Scientist with a deep interest in applying state-of-the-art ML techniques like transformers and diffusion models to problems at the frontier of computational biology.
Design and develop multimodal foundation models for gene regulation and drug response
Integrate chemical structure protein sequences and single-cell transcriptomics into unified models
Adopt and implement latest advances in deep learning (e.g. self-supervised learning FSDP dMoE)
Work in cross-functional teams with biologists engineers and data scientists
Drive hypothesis generation using ML and contribute to novel biological insights
Requirements
PhD or equivalent practical experience in a technical/ML-focused field
Proven experience with deep learning (transformers GNNs SSMs diffusion models etc.)
Strong skills in PyTorch JAX or TensorFlow and scientific libraries like NumPy Pandas
Motivation to apply ML to real-world biological or chemical datasets
Bias toward rapid prototyping and practical outcomes
Bonus Points For:
Prior work in computational biology or drug discovery
Experience with contrastive/multimodal/self-supervised learning
Familiarity with large-scale distributed training and GPU optimizations
The Stack:
Python PyTorch/JAX/TensorFlow
Large-scale ML toolkits (e.g. flash attention FSDP)
Transcriptomics protein sequence data chemical structure modeling
Benefits
- Unlimited PTO
- Monthly lunch allowance remote office setup stipend
- Premium medical dental and vision coverage for employees and dependents
- Hybrid flexibility from South San Francisco or Toronto
PhD or equivalent practical experience in a technical/ML-focused field Proven experience with deep learning (transformers, GNNs, SSMs, diffusion models, etc.) Strong skills in PyTorch, JAX, or TensorFlow, and scientific libraries like NumPy, Pandas Motivation to apply ML to real-world biological or chemical datasets Bias toward rapid prototyping and practical outcomes Bonus Points For: Prior work in computational biology or drug discovery Experience with contrastive/multimodal/self-supervised learning Familiarity with large-scale distributed training and GPU optimizations The Stack: Python, PyTorch/JAX/TensorFlow Large-scale ML toolkits (e.g., flash attention, FSDP) Transcriptomics, protein sequence data, chemical structure modeling
Employment Type
Full Time
