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. 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.
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.
- 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
Benefits
- health care and other insurance benefits (for employee and family)
- retirement benefits
- paid holidays
- vacation
- paid caregiver/parental and medical leave
