Senior AIML Optimization Engineer

Role Summary

Senior AIML Optimization Engineer at GSK Onyx, Seattle Sixth Ave. Responsible for optimizing first-in-class Compute and AIML platforms to accelerate application development, scale computational experiments, and integrate computation with project metadata and performance tracking, across Cloud and High-Performance Computing environments.

Responsibilities

• Serve as a key engineer for the optimization team and contribute technical expertise to teams in closely aligned technical areas such as DevOps, Cloud and Infrastructure
• Lead design of major optimization software components of the Compute and AIML Platforms, contribute to development of production code and participate in both design reviews and PR reviews
• Accountable for delivery of scalable solutions to the Compute and AIML Platforms that supports the entire application lifecycle with particular focus on performance at scale
• Partner with both AIML and Compute platform teams as well as scientific users to help optimize and scale scientific workflows by utilizing deep understanding of both software as well as underlying infrastructure
• Participate or lead scrum team and contribute technical expertise to teams in closely aligned technical areas
• Able to design innovative strategy and way of working to create a better environment for the end users, and able to construct a coordinated, stepwise plan to bring others along with the change curve
• Standard bearer for proper ways of working and engineering discipline, including CI/CD best practices and proactively spearhead improvement within their engineering area

Qualifications

• Bachelor’s, Master’s or PhD degree in Computer Science, Software Engineering, or related discipline
• 6+ years of experience with Bachelor's, 4+ Years of experience with Masters and 2+ years of experience with PhD using specialized knowledge in cloud computing, scalable parallel computing paradigms, software engineering, and CI/CD
• 2+ years of experience in AIML engineering, including large-scale model training and production deployment

Preferred Qualifications

• Deep experience with Python, C/C++, Scala, Java and related toolchains for documentation, testing, and operations/observability
• Deep experience with application performance tuning and optimization in parallel and distributed computing paradigms and related libraries
• Deep expertise in modern software development tools and DevOps practices
• Deep cloud expertise (AWS, Google Cloud, Azure) including infrastructure-as-code and scalable cloud compute technologies
• Expert understanding of AIML training optimization and distributed multi-node training
• Understanding of ML model deployment strategies for multi-GPU, multi-node environments
• Experience with CI/CD implementations using common stacks
• Experience with Docker, Kubernetes, and CNCF ecosystem with deployment tools such as Helm
• Experience with build tools (make, CMake) and optimization at build/compile level
• Experience with agile software development tools such as Jira and Confluence

Skills

• Optimization and performance engineering for large-scale AI/ML platforms
• Cloud and HPC integration, CI/CD, DevOps collaboration
• Programming in multiple languages and familiarity with modern software tooling
• Distributed training and deployment strategies
• Containerization, orchestration, and infrastructure-as-code