Senior Applied AI Engineer

Role Summary

Senior Applied AI Engineer role embedded within cross-functional teams to deliver practical, high-impact AI/ML solutions aligned with GSK’s R&D and business priorities. You will partner closely with scientists, product teams, and domain experts to design, build, and deploy machine learning models and AI-powered tools that accelerate drug discovery, improve decision-making, and enable responsible use of AI across the enterprise. This role is hands-on and consultative in equal measure, evaluating use-case feasibility, prototyping solutions, architecting model integrations, and transferring knowledge so partner teams can operate independently. Location: Cambridge 300 Technology Square; London; Upper Providence, PA.

Responsibilities

• Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options, particularly in scientific domains without active AI/ML engineering efforts.
• Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams to validate ideas quickly and de-risk larger investments.
• Translate complex stakeholder requirements into well-scoped technical solutions with clear success criteria and handover plans.
• Build, train, evaluate, and iterate on ML models for real-world scientific and business problems—including but not limited to NLP/LLM applications, knowledge graphs, causal inference, computer vision, and predictive modeling.
• Package trained models into production-ready services (APIs, containerized deployments) using GSK’s cloud infrastructure (GCP/AWS/Azure).
• Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools where appropriate.
• Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks.
• Embed privacy, ethics, and regulatory considerations into every engagement from the outset.
• Run workshops, seminars, and hands-on training sessions to increase AI literacy across the organization.
• Embed within business/research units for time-limited engagements (typically 6–8 weeks) to accelerate delivery and transfer skills.
• Communicate relevant issues, requests, and opportunities from business units back to AI/ML product leads.

Qualifications

• Required: Bachelor's degree in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or a related quantitative discipline; OR equivalent professional experience as a software/ML engineer.
• Required: 3+ years of professional experience developing and deploying machine learning models (with a Bachelor’s); 2+ years with a Master’s or PhD.
• Required: Expertise in Python, including ML/data science libraries (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
• Required: Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
• Required: Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
• Required: Experience working in cross-functional teams and communicating technical concepts to non-technical stakeholders.
• Required: Experience working in healthcare, pharma, or biological domains.
• Preferred: Experience in pharma, biotech, or life sciences—particularly in drug discovery, genomics, clinical data, or biological data analysis.
• Preferred: Hands-on experience building LLM-based applications, agentic AI systems, RAG pipelines, or multi-agent architectures (e.g., LangChain, LangGraph, AutoGen).
• Preferred: Experience with knowledge graph construction, causal inference, or large perturbation models.
• Preferred: Familiarity with single-cell RNA-seq, spatial transcriptomics, CRISPR assay data, or other high-dimensional biological datasets.
• Preferred: Experience with MLOps practices: CI/CD for ML, model monitoring, experiment tracking (MLflow, Weights & Biases), and reproducible research workflows.
• Preferred: Contributions to open-source ML/AI projects or peer-reviewed publications in applied ML.
• Preferred: Background or demonstrated interest in responsible AI, AI ethics, or model governance.
• Preferred: Strong software engineering practices: version control (Git/GitHub), code review, testing, and documentation.
• Preferred: Experience evaluating and integrating third-party AI/ML vendor tools and platforms.