Director of Machine Learning - Antibodies

About Us 

Alchemab has developed a highly differentiated platform that identifies novel drug targets and therapeutics through analysis of patient antibody repertoires. By combining well-defined patient samples, deep B cell sequencing, and computational analysis, the platform uncovers convergent protective antibody responses among individuals that are susceptible but resilient to specific diseases. Central to this approach are Alchemab’s pioneering antibody foundation models - including AntiBERTa and FAbCon - which are critical to the platform’s success.

Alchemab is building a broad pipeline of protective therapeutics for hard-to-treat diseases, with an initial focus on neurodegeneration, immunology and oncology. The unique patient samples that power Alchemab’s platform are made available through valued partnerships and collaborations with patient representative groups, biobanks, industry partners, and academic institutions.

The Role 

We are seeking an experienced Machine Learning (ML) Director who is passionate about applying machine learning to discover and develop transformative antibody therapeutics. This role is ideal for a scientific leader who combines strategic vision with a desire to stay deeply hands-on - someone who thrives at the interface of research, innovation, and impact.

At Alchemab, ML is a highly collaborative discipline. You will work closely with software engineers, bioinformaticians, assay scientists and antibody engineers to drive the evolution of our platform and accelerate therapeutic discovery. This is a rare opportunity to build on Alchemab’s powerful foundations,  including the world’s largest dataset of antibodies from disease-resilient individuals and cutting-edge multimodal and generative antibody foundation models, to push the limits of what’s possible in antibody discovery.

Responsibilities

• Partner with the VP of Technology to evolve and deliver on Alchemab’s ML strategy.
• Lead and oversee ML efforts across the company, including technical direction, resourcing, and budget management.
• Develop and apply cutting-edge ML approaches to enhance Alchemab’s antibody discovery platform.
• Collaborate closely with wet lab teams to design experiments, generate training data, and validate ML-driven hypotheses.
• Work with software and data teams to build internal tools and applications that democratise access to ML capabilities.
• Communicate complex ML concepts clearly to technical and non-technical audiences across the organisation.
• Represent Alchemab externally at conferences and in publications, positioning the company as a global leader in antibody ML.
• Contribute to patent filings and high-impact publications arising from novel ML methodologies.
• Stay at the forefront of ML and biotech innovation, identifying opportunities to integrate new approaches into the platform.

Essential 

• PhD (or equivalent experience) in Computational Biology, Bioinformatics, Computer Science, Structural Biology, or a related field, with substantial experience in the biopharmaceutical industry.
• Deep expertise in machine learning, including proficiency in Python and PyTorch (or a similar framework).
• Proven track record of scientific innovation and impactful contributions to drug discovery.
• Demonstrated experience shaping scientific or technical strategy within a cross-functional environment.
• Strong leadership skills with experience guiding and influencing diverse teams.
• Highly adaptable, with the ability to prioritise effectively in a fast-paced and dynamic environment.
• Self-starter with a proactive mindset and a drive to take ownership of ML initiatives.
• Passion for Alchemab’s mission and a desire to help build a transformative biotech company.

Desirable 

• Experience applying ML to antibody discovery, protein engineering, or related domains.
• Familiarity with production deployment of ML models, including cloud infrastructure (e.g., AWS).
• Expertise in NLP approaches applied to biological sequences (e.g., protein language models).
• Experience in protein/antibody structural modelling and structure-function prediction.
• A strong publication and/or patent record in machine learning, computational biology, or related areas.

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 [email protected] if you think you can help in the future.