Principal Deep Learning Researcher

The Company

Alchemab has developed a highly differentiated platform which enables the identification of novel drug targets and therapeutics by analysis of patient antibody repertoires. The platform uses well-defined patient samples deep B cell sequencing and computational analysis to identify convergent protective antibody responses among individuals that are susceptible but resilient to specific diseases.

Alchemab is building a broad pipeline of protective therapeutics for hard-to-treat diseases with an initial focus on neurodegenerative conditions and oncology. The highly specialized patient samples that power Alchemabs platform are made available through valued partnerships and collaborations with patient representative groups biobanks industry partners and academic institutions.

At the platforms core is one of the largest and most clinically meaningful antibody datasets in existence: half a billion antibody sequences drawn from thousands of patients and growing. The depth and breadth of proprietary data has enabled Alchemab to develop AntiBERTa and FAbCon two of the leading foundation models for antibody sequences. These assets - unique data at scale combined with state-of-the-art models create the foundation for Alchemabs drug discovery pipeline.

The Role

Reporting in to the ML Director this individual contributor role has real influence over technical direction and will operate and thrive at the interface of research and impact. The Principal Deep Learning (DP) Researcher will develop deep learning models and apply them across Alchemabs antibody discovery pipeline - from representation learning on B-cell receptor repertoires and antigen binding prediction to generative models for sequence optimisation. You will work closely with software developers computational biologists experimental scientists and antibody engineers to turn Alchemabs high-dimensional data into actionable model outputs and testable hypotheses while helping set the ML strategy and supporting the development of colleagues across the organisation. Ultimately the purpose of this role is to deliverinnovative productionready deep learning solutionsthat materially advance Alchemabs antibody discovery and optimisation platform.

Responsibilities

• Develops deep learning architectures for antibody sequence understanding generation and binding prediction
• Partners with the Director of ML to define and deliver Alchemabs ML strategy
• Collaborates with software and DevOps teams to democratize ML capabilities
• Communicates conclusions (not just observations) to both domain experts and non-experts
• Designs rigorous benchmarks to evaluate model performance against experimental ground truth
• Contributes to patent filings and publications arising from novel methodologies
• Stays current with the ML literature; identify and evaluate approaches worth integrating
  
  

Ways of Working

• Contributes to a culture of continuous learning through knowledge sharing mentoring and supporting the development of colleagues
• Takes ownership and accountability for delivering high-quality work balancing scientific curiosity with practical impact
• Communicates complex ideas clearly and constructively adapting style and approach for both technical and non-technical audiences
• Builds scalable and enduring solutions with a focus on creating approaches tools and ways of working that deliver long-term value

Requirements

Essential

• MSc or PhD in Computer Science Mathematics Physics or equivalent quantitative field
• 5 years of experience in designing and training deep learning models with a record of matching architecture to challenging problems
• Evidence of delivering measurable impact through deep learning for example peerreviewed publications adopted by others deployed systems in production experimentally validated methods or patented approaches.
• Strong software engineering fundamentals proficiency in JAX PyTorch or TensorFlow
• Comfort across the scientific Python stack (e.g. NumPy SciPy pandas JAX/PyTorch) to analyse large complex datasets
• Experience using AI coding tools and agentic workflows to prototype refactor and maintain ML codebases with appropriate review and quality controls.
• Demonstrates curiosity about biology and operates effectively in multidisciplinary environments

Desirable

• Successes in applying sequence or structure models to biological data - antibodies TCR proteins or DNA/RNA
• Industry experience in (bio)tech or pharma
• Experience working across scientific disciplines
• Experience deploying ML models in production including cloud infrastructure (e.g. AWS)

NOTE: This job description is not intended to be all inclusive. Employees may perform other related duties as negotiated to meet the ongoing needs of the organisation.

Note to recruitment agencies: we are not looking for assistance at this stage so please contact the HR department only at if you think you can help in the future.

Required Experience:

Staff IC