Senior AI/ML Platform Engineer

GSK•Seattle, WA

About The Position

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 at scale. We 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 DevOps, 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/DevOps 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 towards personal development.

Requirements

Bachelor’s, Master’s or PhD degree in Computer Science, Software Engineering, or related discipline.
6+ years of experience in industry experience in software engineering with a Bachelor’s.
4+ years of experience in 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.

Nice To Haves

Deep knowledge and use of Python programming language including toolchains for documentation, testing, and operations / observability
Deep expertise in modern software development tools / ways of working (e.g. git/GitHub, DevOps 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 TensorFlow as well as external libraries such as Huggingface and/or Deepspeed.
Hands-on experience with frameworks for building agentic AI systems, such as LangGraph, LangChain.
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 DevOps, CloudBuild, Jenkins, CircleCI, GitLab)
Experience in ML workflow orchestration and pipelines with tools such as Vertex Pipelines, MLFlow, etc.
Experience with MLOps tools and model deployments (including LLMs) such as Kubeflow, Vertex AI Predictions, vLLM, Ollama
Deep expertise with Docker, Kubernetes, and the larger CNCF ecosystem including experience with application deployment tools such as Helm
Experience with High-Performance Computing (HPC) at both at software stack as well as hardware level and understanding performance within the HPC systems
Deep familiarity with the tools, techniques, optimizations in AIML and AIML Platform/MLOps space, including engagement with the open-source community (and potentially making contributions to such tools)
Demonstrated excellence with agile software development environments using tools like Jira and Confluence

Responsibilities

Serve as a key engineer for the AIML platform and contribute technical expertise to teams in closely aligned technical areas such as GenAI Platform, DevOps, 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 DataOps, 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.