Applied AI Engineer

About the Role:

As an Applied AI Engineer, you will be 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 AI across the enterprise. This role is hands-on and consultative in equal measure. You will evaluate use-case feasibility, prototype solutions rapidly, architect model integrations, and transfer knowledge so that partner teams can operate independently. You will also contribute to the development of reusable patterns, baseline models, and tested pipelines for common AI/ML tasks within GSK's approved.

Key Responsibilities:

Advisory & Solution Design

• 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.

Model Development & Deployment

• 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.

Knowledge Transfer & Enablement

• 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.

Why you?

Basic Qualifications:

• 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.
• 2+ years of professional experience developing and deploying machine learning models (with a Bachelor's); 1+ years with a Master's or PhD.
• Expertise in Python, including ML/data science libraries (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
• Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
• Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
• Experience working in healthcare, pharma, or biological domains.

Preferred Qualifications:

• Experience in pharma, biotech, or life sciences-particularly in drug discovery, genomics, clinical data, or biological data analysis.
• Hands-on experience building LLM-based applications, agentic AI systems, RAG pipelines, or multi-agent architectures (e.g., LangChain, LangGraph, AutoGen).
• Experience with knowledge graph construction, causal inference, or large perturbation models.
• Familiarity with single-cell RNA-seq, spatial transcriptomics, CRISPR assay data, or other high-dimensional biological datasets.
• Experience with MLOps practices: CI/CD for ML, model monitoring, experiment tracking (MLflow, Weights & Biases), and reproducible research workflows.
• Contributions to open-source ML/AI projects or peer-reviewed publications in applied ML.
• Background or demonstrated interest in responsible AI, AI ethics, or model governance.
• Strong software engineering practices: version control (Git/GitHub), code review, testing, and documentation.
• Experience evaluating and integrating third-party AI/ML vendor tools and platforms.

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases - to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we're committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

If you require an accommodation or other assistance to apply for a job at GSK, please contact the appropriate Recruitment Staff by emailing us at -usrecruitment.adjustments@gsk.com

GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.

Important notice to Employment businesses/ Agencies GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.

Please note that if you are a US Licensed Healthcare Professional or Healthcare Professional as defined by the laws of the state issuing your license, GSK may be required to capture and report expenses GSK incurs, on your behalf, in the event you are afforded an interview for employment. This capture of applicable transfers of value is necessary to ensure GSK's compliance to all federal and state US Transparency requirements. For more information, please visit the Centers for Medicare and Medicaid Services (CMS) website at https://openpaymentsdata.cms.gov/