Principal Deep Learning Researcher at Alchemab Therapeutics in Cambridge

Principal Deep Learning Researcher at Alchemab Therapeutics in Cambridge

Cambridge Full-Time 90000 - 110000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Lead the development of cutting-edge deep learning models for antibody discovery.
  • Company: Join Alchemab Therapeutics, a pioneering biotech in Cambridge.
  • Benefits: Competitive salary and the chance to work with groundbreaking technology.
  • Other info: Collaborate with top scientists and engineers in a dynamic environment.
  • Why this job: Make a real impact on healthcare by transforming antibody discovery.
  • Qualifications: MSc or PhD in a quantitative field with 5+ years in deep learning.

The predicted salary is between 90000 - 110000 £ per year.

Alchemab Therapeutics is a Cambridge-based biotech identifying novel drug targets through deep profiling of half a billion antibody sequences. Using proprietary patient data and state-of-the-art foundation models like AntiBERTa and FAbCon, the company is transforming how protective antibodies are discovered and optimized for therapeutic use.

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.

Location: Cambridge, UK

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.

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.

The ideal candidate:

  • 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.

Principal Deep Learning Researcher at Alchemab Therapeutics in Cambridge employer: Jack & Jill

At Jack & Jill, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture. Our team enjoys a range of benefits including flexible working arrangements, professional development opportunities, and a collaborative environment that encourages creativity and growth. Located in a vibrant area, we offer unique advantages such as access to cutting-edge technology and the chance to work with industry leaders in AI-driven marketing strategies.

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Contact Details:

Jack & Jill Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Principal Deep Learning Researcher at Alchemab Therapeutics in Cambridge

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We think you need these skills to ace Principal Deep Learning Researcher at Alchemab Therapeutics in Cambridge

Deep Learning
Antibody Discovery
JAX
PyTorch
TensorFlow
Model Architecture Design
Sequence Understanding

Some tips for your application 🫡

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