Compute Platform Engineer II

Compute Platform Engineer II

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, increasing productivity and reducing time spent on “data mechanics”. We provide best‑in‑class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top‑tier talent.

Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions in real‑time.

This team builds 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 (HPC). 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.

A Compute Platform Engineer II is a technical contributor who can consistently take a poorly defined business or technical problem, work it to a well‑defined problem/specification, and execute on it at a high level. They have a strong focus on metrics, both for the impact of their work and for its inner workings/operations. They are a model for the team on best practices for software development in general (and their specialization in particular), including code quality, documentation, Dev Ops, and testing.

They ensure robustness of our services and serve as an escalation point in the operation of existing services, pipelines, and workflows.

• Design, build and operate tools, services, workflows, etc. that deliver high value through the solution to key business problems.
• Develop key components of a hybrid on‑prem/cloud compute platform for both interactive and scalable batch computing and establish processes and workflows to transition existing HPC users and teams to this platform.
• Drive 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.
• Optimize design and execution of complex solutions within large‑scale distributed computing environments.
• Produce well‑engineered software, including appropriate automated test suites, technical documentation and operational strategy.
• Ensure consistent application of platform abstractions to maintain quality and consistency with respect to logging and lineage.
• Practice coding best principles and participate in code reviews and partnerships to improve the team’s standards.
• Adhere to the QMS framework and CI/CD best practices and help guide improvements that enhance ways of working.

• Bachelor’s degree in Data Engineering, Computer Science, Software Engineering or a related discipline.
• 4+ years of professional experience after earning a bachelor’s degree.
• 2+ years of professional experience after earning a master’s degree.
• Experience with Python.
• Experience with Cloud platforms.
• Experience with High‑Performance Compute (HPC).

• Knowledge and use of at least one common programming language (Python, C++, Scala, Java) including tool chains for documentation, testing and operations/observability.
• Expertise in modern software development tools/workflows (e.g. git/Git Hub, Dev Ops tools, metrics/monitoring).
• Cloud expertise (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…