Applied AI Engineer
GSK
August 21, 2026
Remote friendly (Cambridge, MA)
United States
IT
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 R&D and business priorities. You will design, build, and deploy machine learning models and AI-powered tools that accelerate drug discovery, improve decision-making, and enable responsible use of AI.
Key Responsibilities:
Advisory & Solution Design
- Provide tailored guidance on AI/ML use cases, feasibility, model selection, and deployment options.
- Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams.
- Translate stakeholder requirements into well-scoped technical solutions with success criteria and handover plans.
Model Development & Deployment
- Build, train, evaluate, and iterate on ML models for scientific and business problems (e.g., NLP/LLM, knowledge graphs, causal inference, computer vision, predictive modeling).
- Package models into production-ready services using GCP/AWS/Azure.
- Develop agentic AI systems, multi-agent architectures, and LLM-based tools.
- Share reusable patterns, baseline models, and tested pipelines.
- Embed privacy, ethics, and regulatory considerations from the outset.
Knowledge Transfer & Enablement
- Run workshops/training to increase AI literacy.
- Embed in business/research units for time-limited engagements (typically 6–8 weeks).
- Communicate issues, requests, and opportunities back to AI/ML product leads.
Basic Qualifications:
- Bachelor’s in CS/ML/Computational Biology/Bioinformatics/Statistics/Engineering (or related) OR equivalent software/ML experience.
- 2+ years ML model development/deployment (with Bachelor’s); 2+ years with Master’s or PhD.
- Python; PyTorch/TensorFlow/JAX/scikit-learn/pandas/numpy.
- Cloud (GCP/AWS/Azure) and containerization (Docker/Kubernetes).
- Strong ML fundamentals (supervised/unsupervised, deep learning, evaluation, feature engineering, experiment tracking).
- Cross-functional communication with non-technical stakeholders.
- Healthcare/pharma/biological domain experience.
Preferred Qualifications:
- Life sciences/pharma experience (drug discovery, genomics, clinical/biological data).
- LLM/agentic AI/RAG/multi-agent hands-on (LangChain/LangGraph/AutoGen).
- Knowledge graphs, causal inference, or large perturbation models.
- Single-cell RNA-seq/spatial transcriptomics/CRISPR assay (high-dimensional biological data).
- MLOps (CI/CD, monitoring, MLflow/Weights & Biases, reproducible workflows).
- Open-source or peer-reviewed applied ML contributions.
- Responsible AI/ethics/governance background.
- Strong software engineering (Git/GitHub, code review, testing, documentation).
- Experience evaluating third-party AI/ML tools.
Benefits/Compensation:
- Annual base salary (US locations): $136,125–$226,875; annual bonus and eligibility for share-based long-term incentive; health care and other insurance, retirement, paid holidays/vacation, and paid caregiver/parental and medical leave.
Application instructions:
- Not specified in the provided text.
As an Applied AI Engineer, you will be embedded within cross-functional teams to deliver practical, high-impact AI/ML solutions aligned with R&D and business priorities. You will design, build, and deploy machine learning models and AI-powered tools that accelerate drug discovery, improve decision-making, and enable responsible use of AI.
Key Responsibilities:
Advisory & Solution Design
- Provide tailored guidance on AI/ML use cases, feasibility, model selection, and deployment options.
- Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams.
- Translate stakeholder requirements into well-scoped technical solutions with success criteria and handover plans.
Model Development & Deployment
- Build, train, evaluate, and iterate on ML models for scientific and business problems (e.g., NLP/LLM, knowledge graphs, causal inference, computer vision, predictive modeling).
- Package models into production-ready services using GCP/AWS/Azure.
- Develop agentic AI systems, multi-agent architectures, and LLM-based tools.
- Share reusable patterns, baseline models, and tested pipelines.
- Embed privacy, ethics, and regulatory considerations from the outset.
Knowledge Transfer & Enablement
- Run workshops/training to increase AI literacy.
- Embed in business/research units for time-limited engagements (typically 6–8 weeks).
- Communicate issues, requests, and opportunities back to AI/ML product leads.
Basic Qualifications:
- Bachelor’s in CS/ML/Computational Biology/Bioinformatics/Statistics/Engineering (or related) OR equivalent software/ML experience.
- 2+ years ML model development/deployment (with Bachelor’s); 2+ years with Master’s or PhD.
- Python; PyTorch/TensorFlow/JAX/scikit-learn/pandas/numpy.
- Cloud (GCP/AWS/Azure) and containerization (Docker/Kubernetes).
- Strong ML fundamentals (supervised/unsupervised, deep learning, evaluation, feature engineering, experiment tracking).
- Cross-functional communication with non-technical stakeholders.
- Healthcare/pharma/biological domain experience.
Preferred Qualifications:
- Life sciences/pharma experience (drug discovery, genomics, clinical/biological data).
- LLM/agentic AI/RAG/multi-agent hands-on (LangChain/LangGraph/AutoGen).
- Knowledge graphs, causal inference, or large perturbation models.
- Single-cell RNA-seq/spatial transcriptomics/CRISPR assay (high-dimensional biological data).
- MLOps (CI/CD, monitoring, MLflow/Weights & Biases, reproducible workflows).
- Open-source or peer-reviewed applied ML contributions.
- Responsible AI/ethics/governance background.
- Strong software engineering (Git/GitHub, code review, testing, documentation).
- Experience evaluating third-party AI/ML tools.
Benefits/Compensation:
- Annual base salary (US locations): $136,125–$226,875; annual bonus and eligibility for share-based long-term incentive; health care and other insurance, retirement, paid holidays/vacation, and paid caregiver/parental and medical leave.
Application instructions:
- Not specified in the provided text.