Ellison Institute of Technology Oxford

Full-stack Engineer

• Design, build, and maintain full stack features for the GPAS Portal and new web applications.
• Contribute to technical architecture for new tools, and help evolve the existing platform’s design and long-term maintainability.
• Collaborate cross-functionally with product, bioinformatics, and platform teams to translate scientific needs into user-focused tools.
• Lead by example through hands-on coding, code reviews, and mentoring junior engineers.
• Ensure scalability and reliability of the overall solution, handling large amounts of genomic, and other multi-modal data.
• Drive best practices for security, testing, CI/CD, and observability across the stack.
• Enhance performance and responsiveness of user interfaces for data-heavy applications.
• Champion usability, ensuring interfaces meet the needs of clinicians and researchers.
• Continuously improve development workflows through automation, tooling, and documentation.

Senior Full Stack Software Engineer – SaaS Platform

Cloud Engineer

• Design, deploy, and maintain Oracle Cloud Infrastructure (OCI) resources including compute, storage, networking, and security.
• Manage environment provisioning and maintenance for Oracle Fusion Cloud (ERP, HCM, SCM) and related services.
• Implement backup, disaster recovery, and high-availability strategies.
• Monitor system performance and ensure SLA compliance.
• Manage environment refreshes (P2T/T2T) and coordinate with Oracle support.
• Develop and maintain infrastructure-as-code (IaC) using Terraform or OCI Resource Manager.
• Automate environment builds, patching, and deployment pipelines.
• Implement CI/CD processes using tools like Jenkins, GitLab, or Azure DevOps.
• Ensure version control, code promotion, and environment synchronisation.
• Apply security best practices across OCI and Fusion applications.
• Manage identity and access control (IAM, roles, policies).
• Ensure compliance with ITIL, SOX, and internal governance frameworks.
• Conduct vulnerability assessments and implement remediations.
• Optimise performance across cloud and on-prem integrations.
• Troubleshoot complex issues involving Fusion, middleware, and OCI services.
• Collaborate with Oracle support for SR management.
• Create runbooks, documentation, and knowledge transfer materials.
• Work with ERP functional teams, developers, DBAs, and integration leads.
• Support test and release activities for Fusion updates and deployments.
• Mentor junior engineers and promote cloud engineering best practices.
• Communicate technical solutions and risks to non-technical stakeholders.

Cloud Engineer, Oracle

Engineer

• **Key Responsibilities:**
• - Design, implement, and maintain scalable HPC infrastructure (cloud and on-prem) to support GBI’s computational research workloads.
• - Evaluate and integrate advanced technologies including GPU/TPU acceleration, high-speed interconnects, and parallel file systems.
• - Manage HPC environments, including Linux-based clusters, schedulers (e.g., Slurm), and high-performance storage systems (e.g., Lustre, BeeGFS, GPFS).
• - Implement robust monitoring, fault-tolerance, and capacity management for high availability and reliability.
• - Develop automation scripts and tools (Python, Bash, Ansible, Terraform, Go, Helm, etc) for provisioning, configuration, and scaling HPC resources.
• - Support reproducible research through containerization (Singularity, Docker, etc), workflow orchestration (Nextflow, Kubernetes, OpenHPC, etc), and MLOps.
• - Collaborate with researchers to address common bottlenecks in their scientific computing workflows.
• - Provide technical support and guidance for job scheduling, workflow optimization, and performance tuning.
• - Collaborate with information security teams to manage user access and protect sensitive research data.
• - *Additional responsibilities at the senior level:*
• - Work with the Head of Scientific Compute on long-term strategy and architecture for GBI’s computing platforms.
• - Collaborate with researchers to understand present and future computational needs and translate them into cloud and HPC requirements and operational policy.
• - Work with HPC and cloud vendors to ensure computational resources at GBI meet the needs of its researchers

High-Performance Computing Engineer – Generative Biology Institute

• Design and develop high-performance, maintainable, and reusable research software to support large-scale biological and biomedical data analysis.
• Collaborate with researchers to translate scientific requirements into robust computational solutions and production-grade tools.
• Build and optimize AI and scientific data processing pipelines, using modern workflow management systems (e.g., Nextflow, Snakemake, CWL, WDL, etc).
• Implement software engineering best practices, including version control (Git, CI/CD), testing, continuous integration, and documentation.
• Benchmark, profile, and optimize computational workloads for maximum performance, scalability, and efficient use of HPC and cloud environments.
• Work closely with the HPC team to ensure seamless deployment and scaling of applications across on-premise and hybrid infrastructures.
• Support reproducibility and transparency in research through containerization (Singularity, Docker, etc) and workflow orchestration (Nextflow, Kubernetes, OpenHPC, etc).
• Contribute to long-term software and infrastructure strategy in alignment with the Head of Scientific Compute.
• Additional responsibilities at the senior level: Collaborate with GBI staff to research novel compute platforms (e.g. novel ASICs, or Quantum Computers).
• Lead research and development into novel scientific workflow technologies (such as cloud-aware workflow schedulers, scientific data management solutions, quantum algorithms, etc)
• Develop APIs, services, and data interfaces that enable interoperability between systems and research platforms.

Research Software Engineer, Generative Biology

Infrastructure Engineer

• **Day-to-day, you might:**
• - Build, operate, and continuously optimise our high-performance GPU training and inference clusters, focusing on robust, high-availability scheduling, isolation, and automated lifecycle management.
• - Drive systems design and implementation for high-throughput data paths, optimising I/O, caching, and data locality across compute and storage (including our current Lustre implementation).
• - Proactively benchmark, profile, and resolve performance bottlenecks across the compute, network, and orchestration layers to maximise efficiency for distributed training and inference.
• - Establish comprehensive observability, resilience, and automated security controls to ensure compliance and robust operation of sensitive research environments.
• - Partner with Research, Data, and Applied teams to forecast capacity and cost for GPU and storage needs, setting quotas and streamlining ML experimentation pipelines.

Senior ML Infrastructure Engineer

Platform Engineer

• **Day-to-day, you might:**
• - Architect, build, and operate our cloud platform, moving infrastructure beyond the initial setup to deliver resilient compute, network, and storage, including full-sized GPU clusters
• - Drive the implementation of highly structured, auditable delivery pipelines (CI/CD/GitOps) using to enforce automated, repeatable infrastructure changes
• - Design and deploy automated governance and security controls using Policy-as-Code (specifically Kyverno and YAML) to ensure strong isolation, protect data, and meet internal audit standards
• - Establish the foundational monitoring, alerting, and telemetry framework required for robust operations, defining clear SLOs, and setting the course for future SRE work
• - Partner with Research and Data teams to build self-service capabilities that efficiently support diverse workloads, from Python notebooks to distributed clusters

Senior Platform Engineer

• Design, develop, and support web applications deployed to support the data platform.
• Own full stack development for tools supporting data management across the full lifecycle, including ingest monitoring, curation, release management, data quality monitoring, and user access control.
• Build and support components to manage ingest from and monitor distributed next-generation sequencing devices, including device telemetry (Kafka, MQTT streaming from Oxford Nanopore NGS devices).
• Design, develop, and maintain platform tools to help bioinformaticians and science teams discover, understand, and access data (e.g., pathogen data catalogue, quality dashboards, management information reporting).
• Support innovation efforts by exploring new technologies such as vector databases to enable search and AI use cases.
• Design, develop, and maintain highly performant, scalable, and secure backend APIs using appropriate technologies (Python, NodeJS).
• Implement and manage data storage solutions including data lakes, data warehouses, and databases underpinning platform tooling (e.g., object storage, Oracle Autonomous Database, PostgreSQL).
• Collaborate with backend engineers to ensure data services and associated APIs meet the needs of platform tools.
• Work with product managers to capture requirements, wireframe solutions, and design user experiences.
• Work with big data technologies such as Kafka, Iceberg, and Parquet, and managed database technologies including PostgreSQL and Oracle vector databases.
• Ensure applications are secure.
• Operate, monitor, and maintain associated Oracle Cloud infrastructure to ensure platform tools are highly available, scalable, and secure.
• Collaborate with DevSecOps engineers to ensure all deployments are automated and follow best-practice CI/CD.
• Ensure solutions are documented and assured through defined architecture governance processes.
• Implement monitoring, logging, and alerting systems to maintain the health and performance of the data platform.
• Line manage and foster the personal development of a highly capable team of front-end engineers focused on the data platform.

Senior Full Stack Software Engineer – Pathogen

Human Resources

• Lead the design, configuration, and implementation of Oracle Fusion HCM modules (Core HR, Talent, Absence, Payroll, Compensation, Recruiting).
• Translate HR business requirements into functional Oracle HCM solutions.
• Serve as the HR functional SME, applying best practices to process design and system configuration.
• Define and maintain governance, data integrity, and security frameworks for HR systems.
• Collaborate with technical teams to ensure seamless integration with other enterprise platforms.
• Manage upgrades, release cycles, testing, and change impact assessments.
• Provide strategic advisory support to HR and IT leadership on Oracle HCM capabilities.
• Develop training and knowledge transfer materials to support user adoption.
• Troubleshoot and resolve complex HR system issues.
• Mentor junior consultants and HR system analysts.

HR Functional Lead – Oracle

Financial Planning and Analysis

• Lead the design, configuration, and implementation of Oracle Fusion Finance modules (GL, AP, AR, Fixed Assets, Cash Management, Expenses, Procurement, Reporting).
• Translate finance business requirements into functional Oracle ERP solutions.
• Act as the Finance SME, ensuring alignment with best practices and compliance standards.
• Define and maintain system governance, internal controls, and data integrity.
• Collaborate with technical teams to ensure seamless integration with other enterprise systems.
• Oversee upgrades, release management, testing, and change impact assessments.
• Provide strategic guidance on Oracle ERP capabilities and best practices.
• Develop training and change management materials to support adoption.
• Resolve issues and drive continuous improvement in financial processes.
• Mentor junior consultants and analysts to build internal capability.

Finance Functional Lead – Oracle

Research Engineer

• Work with large-scale datasets from multiple modalities including biomolecular data (proteins, DNA), 2D/3D images, and graph-structured data.
• Design and implement advanced ML architectures and workflows, including multimodal generative models and physics-based modelling.
• Develop robust model evaluation pipelines.
• Optimize model architectures for performance and reliability.
• Drive best practices in code quality, reproducibility, and collaboration.
• Build and maintain large-scale ML systems that process biomolecule datasets.
• Work with domain specialists to incorporate wet lab data and HPC simulations.
• Help build and maintain large-scale generative models of molecular data.