ML Research Engineer, London

About Isomorphic Labs

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel‑winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world‑leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting‑edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

Research Engineering (Machine Learning), London

We are looking for Research Engineers with different levels of experience—from Mid through to Senior, Staff, Principal or equivalent levels.

Your Impact

This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design. Working in a highly creative, iterative environment, you will partner with scientists and engineers to advance foundational models that will transform the biopharmaceutical world as we know it. You will draw upon your existing engineering and machine‑learning experience while learning from those around you, applying novel techniques and ideas to newly encountered computational biology and chemistry problems.

Responsibilities

Implementation & Optimisation

Translate research concepts into practical implementations by developing and optimising state‑of‑the‑art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation.

Experimentation & Evaluation

Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state‑of‑the‑art machine‑learning methods. Evaluate, tune, and maintain AI/ML models (which includes collecting and preparing data as needed).

Evaluation & Inference

• Implement algorithms and software to analyse and evaluate the performance of AI models.
• Optimise performance of AI/ML models such as Diffusion models, Transformers, GNNs, leveraging a deep understanding of the AI/ML hardware+software stack.
• Advise on how to bring AI/ML models to production and/or integrate them into product offerings, and monitor and refine their behavior.
• Develop specialised tools/frameworks/infrastructure to aid in the work above.

Collaboration & Knowledge Sharing

Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment.

Innovation & Impact

Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms. Execute on independent engineering projects and software development toward research goals.

Essential Skills and Qualifications

• PhD in a technical subject with major engineering component and exposure to AI/ML, or BSc, MSc and 2+ years of specific experience working on ML model development.
• Strong general engineering experience, as evidenced by exposure to one or more of:
• Software design / algorithms, especially for deep learning frameworks
• Modern ML frameworks such as JAX, PyTorch or TensorFlow
• Distributed systems and runtimes
• Compilers (e.g., XLA, Triton, CUDA, Pallas, …)
• Large‑scale model training and serving infrastructure
• Experience in navigating complex research codebases
• Databases and data processing pipelines
• Numerical methods, simulation, optimisation
• Strong fundamentals in mathematics, statistics, linear algebra
• Experience with the full ML research and development lifecycle.
• Strong understanding of ML theory and applications.
• Strong understanding of data structures and algorithms.

Nice to Have

• Interest in chemistry and biology.
• Experience working with biomedical data.
• Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery.

Culture and Values

We are guided by our shared values. It’s not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it.

• Thoughtful – Curiosity, creativity and care. It is about good people doing good, rigorous and future‑making science every single day.
• Brave – Fearlessness, but also initiative and integrity. The scale of the challenge demands nothing less.
• Determined – Confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we.
• Together – Connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere.
• Creating An Extraordinary Company – We believe that to be successful we need a team with a range of skills and talents. We’re building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact.

Hybrid Working

It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call.

Equal Employment Opportunity Statement

We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

#J-18808-Ljbffr