Senior Compute Platform Engineer

Site Name: USA - Pennsylvania - Upper Providence, Cambridge 300 Technology Square, Seattle Sixth Ave, South San Francisco 611 Gateway Blvd

Posted Date: Feb 18 2026

At GSK, we want to supercharge our data capability to better understand our patients and accelerate our ability to discover vaccines and medicines. The Onyx Research Data Platform organization represents a major investment by GSK R&D and Digital & Tech, designed to deliver a step‑change in our ability to leverage data, knowledge, and prediction to find new medicines. We are a full‑stack shop consisting of product and portfolio leadership, data engineering, infrastructure and Dev Ops, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward building a next‑generation, metadata‑ and automation‑driven data experience for GSK’s scientists, engineers, and decision‑makers, providing best‑in‑class AI/ML and data analysis environments, and aggressively engineering our data at scale as one unified asset to unlock its value in real‑time.

The Compute Platform Engineering team is building a first‑in‑class platform of tool chains and workflows that accelerate application development, scale up computational experiments, and integrate all computation with project metadata, logs, experiment configuration and performance tracking over abstractions that encompass Cloud and High‑Performance Computing. This metadata‑forward, CI/CD‑driven platform represents and enables the entire application and analysis lifecycle including interactive development and explorations (notebooks), large‑scale batch processing, observability and production application deployments.

• Designs, builds, and operates tools, services, workflows, etc. that deliver high value through the solution to key business problems.
• Responsible for development of key components of a hybrid on‑prem/cloud compute platform for both interactive and scalable batch computing and establishing of processes and workflows to transition existing HPC users and teams to this platform.
• Responsible for code‑driven environment, applications, and container/image builds as well as CI/CD‑driven application deployments.
• Consult science users on application scalability to PBs of data by having a deep understanding of software engineering, algorithms, and underlying hardware infrastructure and their impact on performance.
• Confidently optimizes design and execution of complex solutions within large‑scale distributed computing environments.
• Produces well‑engineered software, including appropriate automated test suites, technical documentation, and operational strategy.
• Ensures consistent application of platform abstractions to maintain quality and consistency with respect to logging and lineage.
• Fully versed in coding best practices and ways of working, and participates in code reviews and partnering to improve the team’s standards.
• Adheres to QMS framework and CI/CD best practices and helps guide improvements that improve ways of working.
• Provides leadership to team members to help others get the job done right.

• Bachelor’s degree in data engineering, Computer Science, Software Engineering or related discipline.
• 6+ years of professional experience.
• Experience with Python.
• Experience with Cloud.
• Experience with High Performance Compute (HPC).

• Deep knowledge and use of at least one common programming language: e.g., Python, C++, Java, including tool chains for documentation, testing, and operations / observability.
• Deep expertise in modern software development tools / ways of working (e.g. git/Git Hub, devops tools, metrics / monitoring, …).
• Deep cloud expertise (e.g., AWS, Google Cloud, Azure), including infrastructure‑as‑code tools and scalable compute technologies, such as Google Batch and Vertex.
• Experience with CI/CD implementations using git and a common CI/CD stack (e.g., Azure Dev Ops, Cloud Build, Jenkins, Circle
  
  CI, Git Lab).
• Deep expertise with Docker, Kubernetes, and the larger CNCF ecosystem including experience with application deployment tools such as Helm.
• Experience with low‑level application build tools (make, CMake)…