Senior AIML Optimization Engineer
GSK
Full-time
Remote friendly (Seattle, WA)
United States
IT
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Role Summary
Senior AIML Optimization Engineer for Onyx/AIML & Scientific Computing Optimization at GSK. Focused on optimizing Compute and AIML platforms to accelerate application development, scale computational experiments, and integrate computation with project metadata and performance tracking across Cloud and HPC environments.
Responsibilities
- Serve as a key engineer for the optimization team and contribute technical expertise to teams in aligned areas such as DevOps, Cloud and Infrastructure
- Lead design of major optimization software components of the Compute and AIML Platforms, contribute to production code, participate in design reviews and PR reviews
- Deliver scalable solutions for the Compute and AIML Platforms supporting the full application lifecycle with focus on performance at scale
- Partner with AIML and Compute platform teams and scientific users to optimize and scale workflows by leveraging software and underlying infrastructure
- Participate in or lead scrum teams and contribute technical expertise
- Design innovative strategy and create a stepwise plan to improve end-user environment and adoption
- Uphold engineering discipline and CI/CD best practices, driving improvements in the engineering area
Qualifications
- Required: Bachelor’s, Master’s, or PhD in Computer Science, Software Engineering, or related; 6+ years with Bachelor's, 4+ years with Master’s, or 2+ years with PhD in cloud computing, scalable parallel computing, software engineering, and CI/CD
- Required: 2+ years of AIML engineering, including large-scale model training and production deployment
Preferred Qualifications
- Experience with multiple programming languages (e.g., Python, C/C++, Scala, Java) and tooling for documentation, testing, and operations
- Experience with application performance tuning in parallel and distributed computing; knowledge of MPI, OpenMP, Gloo; understanding of hardware, networks, storage
- Experience with modern software development tools (git/GitHub, DevOps tools, metrics/monitoring) and cloud platforms (AWS, Google Cloud, Azure); IaC tools (Terraform, Ansible, Packer)
- Experience with AIML training optimization, distributed multi-node training, and speeding up training jobs
- Understanding of ML model deployment strategies and scalable LLM inference in multi-GPU, multi-node environments
- Experience with CI/CD implementations (Git, Azure DevOps, CloudBuild, Jenkins, CircleCI, GitLab)
- Experience with Docker, Kubernetes, CNCF ecosystem, and deployment tools (Helm)
- Experience with build tools (make, CMake) and optimization at compile time
- Proven ability to work in agile environments using Jira and Confluence