Senior AI​/ML Platform Engineer

The Onyx Research Data Tech organization is GSK's Research data ecosystem which has the capability to bring together, analyze, and power the exploration of data  partner with scientists across GSK to define and understand their challenges and develop tailored solutions that meet their needs. The goal is to ensure scientists have the right data and insights when they need it to give them a better starting point for and accelerate medical discovery.

Ultimately, this helps us get ahead of disease in more predictive and powerful ways.

Onyx is 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".
• Providing 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.

At GSK we see a world in which advanced applications of AI will allow us to develop transformational medicines using the power of genetics, functional genomics, and machine learning. AI will also play a role in how we diagnose and use medicines to enable everyone to do more, feel better, and live longer. It is an ambitious vision that will require the development of products at the cutting edge of AI and Machine Learning.

We're looking for a highly skilled Senior AI/ML Platform Engineer to help us make this vision a reality.

Our AI/ML Platform Engineering team is building a first-in-class platform of tools and services covering MLOps/Dev Ops across Cloud and High-Performance Computing. Our goal is to decrease development time and raise the quality bar on engineering across AI/ML teams and products.

The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career from day one, supporting individuals in dedicating 20% of their time toward personal development.

• Serve as a key engineer for the AIML platform and contribute technical expertise to teams in closely aligned technical areas such as GenAI Platform, Dev Ops, Compute and Cloud.
• Lead design of major software components of the AIML Platform and contribute to development of production code in Python and participate in both design reviews and PR reviews.
• Accountable for key component(s) of AIML Platform with particular focus on usability, reproducibility and performance at scale.
• Integrate with Data Ops, HPC and Data Engineering products for best performance and ease of use in ML training at scale.
• Participate in or lead project teams and contribute technical expertise to teams in closely aligned technical areas.
• Able to design innovative strategies and ways of working to create a better environment for the end users.
• Champion best practices in ways of working and engineering discipline, and proactively contribute to improvements within your engineering area.

• Bachelor's, Master's or PhD degree in Computer Science, Software Engineering, or related discipline.
• 6+ years of industry experience in software engineering with a Bachelor's.
• 4+ years of industry experience in software engineering with a Master's.
• 2+ years of experience in industry and/or academic experience in software engineering with a PhD.
• 2+ years of experience in AIML engineering, including large-scale model training and production deployment.
• Experience with delivering projects primarily using Python.

• Deep knowledge and use of Python programming language including tool chains for documentation, testing, and operations / observability.
• Deep expertise in modern software development tools / ways of working (e.g. git/Git Hub, Dev Ops tools, metrics / monitoring, ...).
• Deep cloud expertise (e.g., AWS, Google Cloud, Azure), including infrastructure-as-code tools (Terraform, Ansible, Packer, ...) and scalable cloud compute technologies, such as Google Batch and Vertex AI.
• Deep hands-on experience with ML frameworks such as PyTorch or Tensor Flow as well as external libraries such as Huggingface and/or Deepspeed.
• Hands-on experience with frameworks for building agentic AI systems, such as Lang Graph, Lang Chain.
• Experience with ML application performance tuning and optimization, both for ML training and inference/deployment, including large scale multi-GPU, and/or multi-TPU multi-node distributed training for large models such as LLMs.
• 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).
• Experience in…