Cellarity seeks to fundamentally redesign the way drugs are created by shifting the focus from a single target to the underlying cellular dysfunction, unraveling the complexity of disease biology and creating medicines that were never before possible. The Machine Learning Scientist will play a critical role in bringing new medicines to patients by developing and advancing foundation models that capture biological networks driving cellular dysfunction and integrate diverse perturbation modalities to accelerate drug discovery.
Requirements
- Strong foundation in statistics, deep learning and generative AI.
- Proficient in Python and scalable ML frameworks (e.g. PyTorch, Hugging Face)
- Experience with high-dimensional biological data analysis (bulk/single-cell RNA-seq, Gene regulatory networks, PPI networks, multi-omics integration).
- Experience with chemical / CRISPR perturbation screen data (eg: Perturb-seq).
- Experience developing deep generative models for representation learning.
- Experience efficiently training and fine-tuning foundation models on omics data.
- Experience with cloud computing (AWS/GCP) and MLOps best practices.
Responsibilities
- Design and implement novel algorithms and architectures for large-scale foundation models (e.g. Diffusion models, Transformers, VAEs) using single-cell RNA-seq and other modalities.
- Develop mechanistic interpretability methods to infer gene networks and regulatory mechanisms via attention, graph-based, and/or causal representation methods.
- Build state-of-the-art perturbation models using multi-modal perturbation (small molecules, CRISPR, cytokines) and phenotypic data.
- Pre-train, post-train and deploy generative AI models using proprietary datasets, and distributed training and inference on cloud platforms.
- Establish clinically relevant benchmarking and evaluation frameworks to assess context generalization and guide model improvements.
- Stay current with the latest research in foundation models, representation learning (across biology, NLP, vision, and audio), and perturbation modeling.
- Collaborate with interdisciplinary scientists from biology, chemistry, and technology teams to translate research questions into cutting-edge ML solutions.
Other
- PhD in Computer Science, Computational Biology, or related field, OR Master's degree with 3+ years of relevant ML research or industry experience.
- Excellent communication skills and ability to work in interdisciplinary teams.
- Ability to work in a fast-paced, multidisciplinary, biotech environment.
- Communicate technical concepts clearly to diverse scientific audiences.
- Contribute to strategic roadmap planning and technical decisions.
