As a Principal Deep Learning Researcher at Alchemab Therapeutics, you will lead the development of state-of-the-art models for antibody discovery. You will leverage one of the world’s largest antibody datasets to design architectures for sequence understanding and binding prediction, bridging the gap between high-dimensional data and life-saving experimental hypotheses.
Principal Deep Learning Researcher at Alchemab Therapeutics
Join Alchemab Therapeutics as a Principal Deep Learning Researcher to lead the development of next-generation foundation models for antibody discovery. Leveraging a proprietary dataset of 500 million antibody sequences, you will refine models like AntiBERTa and FAbCon to identify protective antibodies in resilient patients. This staff-level role offers the unique chance to influence ML strategy, mentor colleagues, and work at the absolute forefront of AI-driven drug discovery in Cambridge, UK. Help turn high-dimensional clinical data into life-saving therapeutics within a high-impact, hybrid research environment.
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Location
Cambridge, United Kingdom
Compensation
Not Disclosed
Company
Alchemab Therapeutics
Role overview
Alchemab Therapeutics is pioneering the next generation of antibody therapeutics for hard-to-treat diseases, with an initial focus on neurodegenerative conditions and oncology. The company has developed a highly differentiated platform that harnesses the power of the immune system by analyzing patient antibody repertoires. Using well-defined patient samples, deep B cell sequencing, and computational analysis, Alchemab identifies convergent protective antibody responses among individuals who are susceptible but resilient to specific diseases, such as long-term cancer survivors. This unique approach enables the discovery of novel drug targets and therapeutics from antibodies shared across resilient populations.
What you will do
- Develop and deploy deep learning architectures for antibody sequence generation, representation learning, and antigen binding prediction using JAX, PyTorch, or TensorFlow.
- Partner directly with the Director of ML to define the technical roadmap and collaborate with software teams to democratize ML capabilities across the organization.
- Design and execute rigorous benchmarks to evaluate model performance against real-world experimental ground truth from Alchemab’s wet-lab facilities.
Who this is a fit for
- Holds an MSc or PhD in a quantitative field with 5+ years of experience designing and training complex deep learning models in production or research.
- Demonstrates a strong track record of impact through high-impact publications or deployed systems, with deep proficiency in modern ML frameworks and coding agents.
- Possesses a genuine curiosity for biology and the ability to communicate sophisticated technical conclusions to cross-functional teams of scientists and engineers.
Why this role is remarkable
- Work with one of the world’s largest proprietary antibody datasets, featuring over half a billion sequences drawn from thousands of patients.
- Contribute to the evolution of industry-leading foundation models like AntiBERTa and FAbCon, setting the technical direction for antibody generative modeling.
- Influence Alchemab Therapeutics’ long-term ML strategy as a staff-level individual contributor operating at the cutting edge of biology and deep learning.
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