Applied AI Engineer

GSK

Applied AI Engineer developing machine learning solutions for GSK’s biopharma research and business teams. Building deployable models, LLM tools, and responsible AI capabilities.

Posted 8/20/2026full-timeUpper Providence • Pennsylvania • 🇺🇸 United StatesJuniorMid-Level💰 $136,125 - $226,875 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models, with a strong focus on Python and relevant frameworks. Capable of translating complex stakeholder requirements into actionable technical solutions while embedding ethical considerations and fostering AI literacy.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingGCP, AWS, or Azure ExperienceMLOps PracticesHealthcare or Pharma Domain Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningFeature EngineeringModel EvaluationSupervised LearningUnsupervised LearningExperiment TrackingKnowledge Graph ConstructionCausal InferenceLarge Perturbation Models

Soft Skills

Cross-Functional Team CollaborationTechnical Communication

Tools & Technologies

PyTorchTensorFlowJAXScikit-learnPandasNumpyDockerKubernetes

Industry Keywords

HealthcarePharmaBiotechLife SciencesDrug DiscoveryGenomicsClinical DataBiological Data AnalysisResponsible AIAI Ethics

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesNumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options
Co-design prototypes and proof-of-concepts with product and domain teams
Translate stakeholder requirements into scoped technical solutions with success criteria and handover plans
Build, train, evaluate, and iterate on ML models for scientific and business problems
Package trained models into production-ready APIs and containerized deployments using GSK cloud infrastructure
Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools
Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks
Embed privacy, ethics, and regulatory considerations into engagements
Run workshops, seminars, and hands-on training to increase AI literacy
Embed within business and research units for typically 6–8 week engagements to accelerate delivery and transfer skills
Communicate issues, requests, and opportunities from business units to AI/ML product leads

Requirements

What you’ll need

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; 2+ years with a Master’s or PhD
Expertise in Python, including PyTorch, TensorFlow, JAX, scikit-learn, pandas, and numpy
Experience with GCP, AWS, or Azure and Docker and Kubernetes
Strong understanding of supervised and 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
Experience in pharma, biotech, or life sciences, particularly 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
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 including CI/CD for ML, model monitoring, experiment tracking, 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 including Git/GitHub, code review, testing, and documentation
Experience evaluating and integrating third-party AI/ML vendor tools and platforms

Benefits

Comp & perks

Annual bonus
Eligibility to participate in share-based long-term incentive program
Health care and other insurance benefits for employee and family
Retirement benefits
Paid holidays
Vacation
Paid caregiver/parental and medical leave