Data Engineer (Forward Deployed)

Join us at EIT:

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 .

Scientific Compute and Data Team

The Scientific Compute and Data team builds the compute, data and tooling foundation behind EIT's science. We run the cloud and GPU infrastructure that the models train on, the data infrastructure that instruments, robots and researchers use, and the data products and shared data models that let one team's results be built on by the next. We provide hands-on expertise in platform engineering, data engineering, and data management, and create common tooling for every lab to accelerate their experiments across gene editing, battery chemistry, plant biology and more.

Your Role:

At EIT we are seeking a Forward Deployed Engineer to join our Scientific Compute and Data Team. Forward Deployed Data Engineers are a key interface between our core data systems and the research and engineering projects across EIT. You’ll work side-by-side with scientists and engineers in frontier AI and robotics, generative and plant biology, or pathogen surveillance. You will disseminate data engineering best practices into the institutes, ensuring that datasets are accurate, reproducible, versioned-controlled, well-structured, and ready for ML. You will help scientists turn raw information from various sources into high-quality resources, ensuring our technical foundations support the next generation of discovery.

This is a hands-on role for those who thrive on collaborating directly with researchers and engineers, solving problems quickly, and turning complex research requirements into scalable, reliable data pipelines while contributing to the broader EIT platform. We are looking for clear, respectful communicators who are comfortable bringing their own expertise into diverse groups.

Day-to-Day, You Might:

• Partner with scientists and engineers to deliver robust, reproducible data pipelines that meet research needs across disciplines.
• Own ingestion, storage, curation, and transformation of diverse biological datasets and formats, such as structured/tabular data, unstructured text, high I/O formats like LMDB/Arrow/HDF5, or domain-specific formats such as fastq/fasta and cif.
• Package and deploy code in research environments using containers (e.g. Docker).
• Scale processing across distributed cloud warehouses/storage via container orchestration (e.g. Kubernetes), High-Performance GPU Compute (e.g. Slurm), or distributed compute frameworks (e.g. Spark, Ray).
• Contribute to an engineering culture that values maintainability, testing, robust system design, and deep collaboration, but allows flexibility for rapid prototyping and responsiveness to changing landscapes.

Requirements

What Makes You a Great Fit:

Nobody checks every box - if you are excited about this role and think you could contribute, we encourage you to apply.

• You have strong programming experience in Python, and value code quality, reliability, and readability as much as performance.
• You have a deep understanding of data storage and manipulation: relational systems, sharding and parallelisation, indexing, scalability.
• You have experience working on cloud compute platforms and varied Linux environments.
• You have worked at scale, especially on distributed/parallelised data systems.
• You promote good Data Engineering and Data Management practices as a means of enabling good science – reproducibility, version-control, schema design, lineage, single-source of truth.
• You think in terms of systems and longevity, not just one-off ETL scripts, and embrace end-to-end ownership from low-level performance to user interfaces.

Our Forward Deployed Engineers will have the opportunity to work on diverse projects, and to expand their skillset, but will add particular value when they can match their data engineering expertise with domain knowledge relevant to a project. We would value:

• Experience preparing data for ML: pre-training, post-training, and evaluations.
• Scientific background, with experience or qualifications in genomics, proteomics, or other related fields.
• Experience in bioinformatics and use of industry-standard tooling such as NextFlow.

Benefits

We offer the following benefits:

• Salary dependent on experience + travel allowance + bonus
• Enhanced holiday. Our annual leave allowance is 25 days plus 8 bank holidays and an additional 3 days between Christmas and New Year. You will also have the opportunity to purchase an additional 5 days annual leave in January and July.
• Pension - Employer contribution 7.5%, minimum employee contribution 5%
• Life Assurance.
• Income Protection
• Private Medical Insurance as standard for you, your partner and any dependents. Including hospital Cash Plan
• Employee discounts
• Electric car scheme
• Nursery Salary Sacrifice scheme
• Cycle to Work Scheme
• Family Planning
• Neurodiversity support including advise and assessments
• Coaching & Therapy services

Working together – what it involves:

• 2 days per week in Oxford office, 1 day per week in employee’s choice of Oxford or London office, 2 days per week work from any UK location.
• You must have the right to work permanently in the UK with a willingness to travel as necessary. In certain cases, we can consider sponsorship, and this will be assessed on a case-by-case basis.