Senior Applied AI Engineer
GSK
5 hours ago
Remote friendly (Collegeville, PA)
United States
$160,050 - $266,750 USD yearly
IT
Senior Applied AI Engineer
Key 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 (e.g., NLP/LLM, knowledge graphs, causal inference, computer vision, predictive modeling).
- Package trained models into production-ready services (APIs, containerized deployments) using GSK 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 to increase AI literacy.
- 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.
Basic Qualifications
- Bachelor’s degree in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or related quantitative discipline; or equivalent experience as a software/ML engineer.
- 3+ years developing and deploying machine learning models (with a Bachelor’s); 2+ years with a Master’s or PhD.
- Expertise in Python (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
- Experience with cloud platforms (GCP/AWS/Azure) and containerization (Docker, Kubernetes).
- Strong ML fundamentals (supervised/unsupervised learning, deep learning, model evaluation, feature engineering, experiment tracking).
- Experience working in cross-functional teams and communicating with non-technical stakeholders.
- Experience in healthcare, pharma, or biological domains.
Preferred Qualifications
- Experience in pharma/biotech/life sciences (drug discovery, genomics, clinical data, or biological data analysis).
- Hands-on experience with LLM-based applications, agentic AI systems, RAG pipelines, or multi-agent architectures (e.g., LangChain, LangGraph, AutoGen).
- 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.
- MLOps practices (CI/CD, model monitoring, experiment tracking such as MLflow/Weights & Biases, reproducible workflows).
- Open-source ML/AI contributions or peer-reviewed applied ML publications.
- Interest in responsible AI, AI ethics, or model governance.
- Strong software engineering practices (Git/GitHub, code review, testing, documentation).
- Experience evaluating/integrating third-party AI/ML vendor tools and platforms.
Key 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 (e.g., NLP/LLM, knowledge graphs, causal inference, computer vision, predictive modeling).
- Package trained models into production-ready services (APIs, containerized deployments) using GSK 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 to increase AI literacy.
- 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.
Basic Qualifications
- Bachelor’s degree in Computer Science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or related quantitative discipline; or equivalent experience as a software/ML engineer.
- 3+ years developing and deploying machine learning models (with a Bachelor’s); 2+ years with a Master’s or PhD.
- Expertise in Python (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
- Experience with cloud platforms (GCP/AWS/Azure) and containerization (Docker, Kubernetes).
- Strong ML fundamentals (supervised/unsupervised learning, deep learning, model evaluation, feature engineering, experiment tracking).
- Experience working in cross-functional teams and communicating with non-technical stakeholders.
- Experience in healthcare, pharma, or biological domains.
Preferred Qualifications
- Experience in pharma/biotech/life sciences (drug discovery, genomics, clinical data, or biological data analysis).
- Hands-on experience with LLM-based applications, agentic AI systems, RAG pipelines, or multi-agent architectures (e.g., LangChain, LangGraph, AutoGen).
- 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.
- MLOps practices (CI/CD, model monitoring, experiment tracking such as MLflow/Weights & Biases, reproducible workflows).
- Open-source ML/AI contributions or peer-reviewed applied ML publications.
- Interest in responsible AI, AI ethics, or model governance.
- Strong software engineering practices (Git/GitHub, code review, testing, documentation).
- Experience evaluating/integrating third-party AI/ML vendor tools and platforms.