ML Scientist; Senior & Non Senior - 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, policy makers, and entrepreneurs 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.

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The Generative Biology Institute (GBI) at the Ellison Institute of Technology (EIT) aims to overcome two major challenges in making biology engineerable:
1) the ability to precisely synthesize entire genomes, and
2) understanding which DNA sequences will create biological systems that perform desired functions. Solving these challenges will unlock the potential of biology for transformative solutions in health, sustainability, agriculture, and more. GBI will house 30 groups and over 600 researchers, supported by cutting‑edge facilities and sustained funding to address global challenges and advance biology engineering.

At EIT we are seeking an experienced and detailed orientated ML Scientist (Senior & Non Senior) to develop AI and machine learning systems that drive and catalyze 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 for two roles at regular or senior level depending on experience.

• 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 know‑how 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

• 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, visualising, and analysing…