Bio-ML Scientists (All Levels) - Generative Biology Institute

At the Ellison Institute of Technology (EIT), we're on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, engineers, researchers, educators and innovators to tackle humanity's greatest challenges in four transformative areas:

• Health, Medical Science & Generative Biology
• Food Security & Sustainable Agriculture
• Climate Change & Managing CO₂
• Artificial Intelligence & Robotics

This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you'll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Together, we push boundaries, embrace complexity, and create solutions to scale ideas from lab to society. Explore more at www.eit.org.

Welcome to the Generative Biology Institute:

Led by Founding Director Jason Chin, the Generative Biology Institute (GBI) at the Ellison Institute of Technology is tackling the key challenges in making biology engineerable, and thereby unlocking the unrivalled power of biology for the benefit of humanity.

The vision of the GBI is to lay the foundations for engineering biology, and unlock its potential for good. To achieve this, we must overcome two key challenges. First, we need the ability to write in the natural language of biology, enabling the rapid and scalable synthesis of entire genomes with precision. Second, we must understand what to write - determining which DNA sequences will generate biological systems that perform the desired functions. Addressing these challenges will allow us to harness the full power of biology to create transformative solutions across health, agriculture, clean energy and more.

GBI will have sustained and substantial funding to support the unique scale and ambition of its ground-breaking vision for engineering biology. GBI researchers will also be supported by cutting-edge technology hubs including mass spectrometry, flow cytometry, sequencing, automation, imaging, and bioprocessing. GBI will also have access to substantial compute resources that can be leveraged to further accelerate progress, including scientific compute, bioinformatics, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial applications, where appropriate.

The Generative Biology Institute commenced operations in 2025, occupying newly renovated bespoke space in the Oxford Science Park. The team will later move to a purpose-made facility in the Oxford Science Park, currently under construction. Once complete, this state-of-the-art facility will include more than 40,000 m² of research laboratory and office space. It will house over 30 groups and up to 600 employees at scale, focused on solving the two critical challenges in making biology engineerable and applying the solutions to addressing the global challenges encapsulated in EIT's Humane Endeavors.

Your Role:

At EIT we are seeking an experienced and detail orientated Bio-ML Scientist (Senior & Non Senior) to develop AI and machine learning systems that drive and catalyse GBI's scientific aims, working alongside researchers and platform staff to address key questions in biological sequence design and discovery. The post-holder will work with multiple data modalities with a focus on sequence-to-function modelling, prediction and optimisation.

This is an exceptional opportunity to join a new unit at the forefront of AI/ML and synthetic biology with access to exceptional facilities and expertise. We are looking for colleagues who thrive in a team and care deeply about biological questions, hypotheses, and a biology-centric approach to AI/ML engineering. The role requires broad technical expertise in applied machine learning and prior exposure to synthetic biology design tasks in collaboration with wet lab scientists. Our team ethos is based on mutual learning, strong peer-to-peer support, and a deep sense of scientific curiosity and ambition. We are hiring at regular or senior level depending on experience.

Key Responsibilities:

• Design and build AI and machine learning systems to address GBI's research challenges in synthetic biology, genome design, and molecular evolution.
• Lead and contribute to collaborative projects with GBI researchers, staff, and external collaborators.
• Work closely with GBI wet lab scientists in co-creation of research projects and development of fit-for-purpose computational solutions.
• Provide expert machine learning knowhow to GBI researchers and scope novel avenues of research.
• Interact with the Bioinformatics and Scientific Compute platform teams to support the development of GBI data flows and MLOps.
• Ensure compliance with best practices in ML engineering, including robust and reproducible training pipelines, as well as versioning and documentation of data, models, and code.
• Keep abreast of progress in AIxBio and make use of strategic learning opportunities.
• Lead and contribute to research publications in prestigious venues.
• Organise and prioritise work, operating at the highest standard in the face of multiple competing deadlines.
• Promote and champion EIT and the work of the GBI, representing the institute at functions and public events.

Requirements

Essential Knowledge, Skills and Experience:

• PhD degree in a suitable field including, but not limited to, mathematics, computer science, molecular biology, computational biology, engineering, or related discipline. Desirable: at least 2 years of industry or postdoctoral in similar roles.
• Experience in building AI or machine learning models for biological design tasks, involving processing, visualizing, and analysing various data modalities in collaboration with wet lab scientists and using a breadth of methods and architectures (such as classic statistical learning, genomic/protein foundation models, deep learning, genome-scale metabolic models, representation learning, multi-modal learning, active learning).
• Ability to abstract high-level biological questions and translate them into actionable machine learning tasks, evidenced by previous achievements in a comparable industry role, or a promising publication record in scientific journals and technical conferences.
• Ability to learn quickly and dive into a range of problem spaces and computational methods.
• Ability to work and communicate with and within diverse and multidisciplinary teams.
• Fluency in one or more scientific programming languages (Python, R, Julia, etc) with experience in best practices in machine learning, including documentation.
• Excellent written and oral communication skills for diverse audiences, including colleagues without a computational background.
• Excellent time management skills across competing tasks requiring rapid context switching.