Applied AI Engineer
$136,125–$226,875 year
On-siteUpper Providence, Pennsylvania, United States
Job Summary
Provide tailored guidance on AI/ML use cases, feasibility, and deployment options to business units, particularly in scientific domains. Co-design prototypes and proof-of-concepts with product and domain teams to validate ideas and de-risk investments. Build, train, evaluate, and iterate on machine learning models for real-world problems, including NLP, knowledge graphs, and predictive modeling. Package trained models into production-ready services using GSK's cloud infrastructure and develop agentic AI systems. Share reusable patterns, baseline models, and tested pipelines while embedding privacy, ethics, and regulatory considerations. Run workshops and training sessions to increase AI literacy across the organization.
Required Qualifications
- Bachelor's degree in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or a related quantitative discipline
- Equivalent professional experience as a software/ML engineer
- 2+ years of professional experience developing and deploying machine learning models
- 2+ 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 cross-functional teams and communicating technical concepts to non-technical stakeholders
- Experience working in healthcare, pharma, or biological domains
Desired 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
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