GSK is looking to leverage advanced applications of machine learning and AI to develop novel therapies for existing and emerging diseases, aiming for personalized drugs with better outcomes, reduced cost, and fewer side effects. The AI/ML Engineer will contribute to developing products and solutions at the cutting edge of machine learning and AI to achieve this vision.
Requirements
- 2+ years of experience in machine learning and software engineering best practices.
- 2+ years of experience with working in a collaborative CI/CD software development environment, including use of git.
- 2+ years of experience with developing, implementing, and training deep learning models with PyTorch, Tensorflow, or other deep learning frameworks.
- Experience working with high-content imaging and diverse multi-omics
- Track record of writing software in a team in industrial environments or open-source projects.
- Track record of projects or peer-reviewed publications at the intersection of machine learning and life sciences
- Mentality of commit early and often, metrics before models, and shipping high quality production code.
Responsibilities
- Carry out product-driven research on novel machine learning methods to analyze terabytes of internal multi-modal high-content data.
- Design approaches to deconvolve real biological signals from confounding effects that are inherent in high-throughput biological data.
- Leverage internal high performance computing cluster and cloud compute to train and productionize our models at scale.
- Work closely with domain experts on cross-disciplinary teams to generate actionable insights that impact target identification, hit identification, and safety testing.
- Contribute to our developing codebase with well-tested, production-ready code.
Other
- PhD or master's in computer science, engineering, applied mathematics, machine learning, or equivalent practical experience.
- 2 + year experiences in cell imaging is required for master degree holder.
- Competitive candidates will have in-depth knowledge of machine learning with a track record of developing deep learning models for solving challenging real world scientific problems.
- They should be comfortable with writing quality, well-documented, and well-tested code in the AI/ML space and operate in an agile environment.
- Knowledge in disease biology, molecular biology, and biochemistry.
