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About This Role
At GSK, we unite science, technology and talent to get ahead of disease together. Our ambition is to positively impact the health of 2.5 billion people over the next decade. We are building a future where state-of-the-art software, AI, and machine learning enable us to discover new therapies and personalized medicines that drive better outcomes for patients—at reduced cost and with fewer side effects.
The Applied AI team sits at the intersection of business need and technical capability within the AI/ML department. We directly support business units with AI/ML-related challenges, acting as ambassadors for responsible AI across the organization. This role is your opportunity to work at the frontier of applied machine learning in one of the world’s leading biopharma companies, translating cutting-edge AI research into real scientific and business impact
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 use of 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); 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.
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.
Skills
Artificial Intelligence (AI), Artificial Intelligence Ethics, Artificial Neural Networks (ANNS), Classification Models, Deep Learning, Intelligent Automation (IA), Machine Learning (ML), Model Evaluation, Model Validation, Predictive Modeling, Probabilistic Modeling, Python (Programming Language), Test Documentation* If you are based in Cambridge, MA; Waltham, MA; Rockville, MD; or San Francisco, CA, the annual base salary for new hires in this position ranges $136,125 to $226,875. The US salary ranges take into account a number of factors including work location within the US market, the candidate’s skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process.
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
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 - [email protected]
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.
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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/
Salary Context
This $136K-$226K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At GSK, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($181K) sits 14% below the category median. Disclosed range: $136K to $226K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
GSK AI Hiring
GSK has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span London, England, UK, Philadelphia, PA, Collegeville, PA, US. Compensation range: $226K - $226K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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