We are seeking a dynamic scientific and technical leader to build and direct a newDiscovery Applied AI/ML group — a team dedicated to transforming how we discover medicines by embedding state-of-the-art AI/ML directly into our discovery workflows, platforms, and decision-making.
We designed this role for someone with a desire to advance scientific knowledge and harness the revolution in AI/ML, automation, and predictive sciences to deliver measurable impacts across the drug discovery procress on the success and progression of our medicine discovery portfolio.
This group will operate as the applied AI/ML engine and thought partner for GSK’s Discovery functions, working in close partnership with Discovery Data Sciences and other R&D teams.In partnership with the Discovery Data Sciences whichowns the core predictive modeling, analytics, and data science support across modalities, Discovery Applied AI/ML will:
1. Focus on AI/ML innovation, engineering, and productization (e.g., generative design tools, active learning loops, LIAL frameworks)
2. Rapidly translate emerging AI/ML methods and technologies into robust, deployed solutions
3. Serve as a strategic AI/ML partner to discovery line leaders, RTech teams, and platform owners
You will lead a unified, cross-functional team of applied ML scientists, ML engineers, and AI product leaders who partner deeply with research units to solve their most critical challenges.
This position is based 2–3 days per week at one of our R&D sites (e.g., Upper Providence, PA; Cambridge Tech Square, MA; Stevenage, UK; or Heidelberg, Germany).
Key Responsibilities
1. Strategic Vision & Organizational Architecture
4. Define and lead the applied AI/ML strategy for Discovery, aligned with the broader Data, Automation, and Predictive Sciences (DAPS) and Discovery Data Sciences roadmaps.
5. Establish and clearly articulate a vision for a research- and service-oriented applied AI/ML organization focused on:
6. Creation, evaluation, and deployment of state-of-the-art AI/ML techniques and platforms
7. Direct enablement of automated discovery paradigms, including Lab-in-an-Automated-Loop (LIAL) and other closed-loop experimentation systems
8. Design and implement an organizational model that integrates:
9. Applied ML research (novel architectures, generative models, active learning)
10. AI/ML engineering & platformization (scalable services, APIs, reusable components)
11. AI product management (use-case discovery, user-centric design, adoption)
12. Develop a multi-year strategic roadmap for how applied AI/ML will:
13. Increase the Probability of Technical and Regulatory Success (PTRS)
14. Shorten design–make–test–analyze cycles
15. Enhance decision quality across discovery programs
2. Portfolio Impact & Scientific Partnership
16. Act as the primary applied AI/ML partner to discovery and RTech line leaders, embedding your team into portfolio projects across therapeutic areas and modalities.
17. Work in tight coordination with Discovery Data Sciences to:
18. Identify high-impact problems where AI/ML can drive step-change improvements
19. Decide when to advance from prototype to platform
20. Ensure clear delineation between core data science support (DDS) and advanced/applied AI/ML builds (DAI/ML).
21. Lead problem-framing and solution design for AI/ML use cases:
22. Generative design (molecules, proteins, biologics, modalities)
23. Active learning and optimization in high-throughput screening
24. AI-guided experiment planning and lab scheduling
25. Multi-modal integration for mechanism-of-action and target/context selection
26. Establish and maintainstage-gated, fail-fast frameworks for AI/ML projects:
27. Clear hypotheses
28. Success metrics tied to scientific or operational outcomes
29. Criteria for scale-up, sunset, or pivot
30. Communicate results and impact effectively to diverse audiences:
31. Scientific stakeholders (detailed methods, data, and models)
32. Platform & engineering teams (interfaces, requirements, performance)
33. Executives (value, risk, investment needed, portfolio impact)
3. AI/ML Innovation, Engineering & Research Leadership
34. Drive a culture of pioneering applied AI/ML research, with emphasis on:
35. Generative models (e.g., diffusion models, VAEs, transformers) for molecular and protein design
36. Active learning, Bayesian optimization, reinforcement learning for closed-loop experimentation
37. Foundation models and large-scale representation learning across biological, chemical, and omics data
38. Multi-modal integration (e.g., sequence, structure, imaging, omics, real-world data)
39. Allocate protected time and resources for your team to:
40. Explore emerging methods
41. Run exploratory pilots with clear transition criteria
42. Contribute to publications, preprints, and community engagement (as appropriate)
43. Ensure that promising methods are translated into robust, maintainable solutions:
44. Collaborate with engineering and platform teams to build scalable APIs, services, and tools
45. Establish best practices for model lifecycle management (MLOps) in partnership with R&D Digital & Tech
46. Implement reproducible and compliant workflows for model development, validation, and monitoring
47. Champion ethical, transparent, and compliant AI/ML:
48. Ensure appropriate safeguards, interpretability, and documentation
49. Work with Risk & Compliance to align with regulatory and internal governance requirements
4. Platform & Technology Build Leadership
50. Partner with Discovery Data Sciences, Discovery Engineering & Integration, Automation, Cheminformatics, Protein Design & Informatics, and R&D Digital & Tech to:
51. Architect and deliver AI-augmented platforms for design, analysis, and decision support
52. Enable LIAL and automated discovery frameworks, where AI/ML models actively inform experiment selection and optimization
53. Co-lead priority technology builds, ensuring:
54. AI/ML capabilities are designed as reusable components and services
55. Seamless integration with data platforms (e.g., Onyx, QEL) and lab automation systems
56. Alignment with enterprise standards for data, APIs, security, and compliance
57. Define and track technical and business KPIs for AI/ML systems:
58. Model performance and robustness
59. Usage and adoption metrics
60. Impact on cycle times, cost, and decision quality
5. Thought Partnership & Internal Advocacy for AI/ML
61. Serve as a trusted thought partner to Discovery leadership on AI/ML:
62. Help shape the AI/ML aspects of discovery strategy
63. Advise on where to buy, build, or partner for AI/ML capabilities
64. In collaboration with the technology evaluation / innovation roles, continuously scan the external AI/ML landscape:
65. Evaluate emerging tools, platforms, and models for applicability
66. Recommend strategic collaborations or partnerships where they can accelerate impact
67. Provide training, education, and evangelism:
68. Help non-ML experts understand what AI/ML can and cannot do
69. Develop materials, seminars, and office hours for scientists and leaders
6. Talent & Culture Development
70. Build, lead, and mentor a high-performing global team of:
71. Applied ML scientists
72. ML/AI engineers
73. AI product managers / technical program leads
74. Foster a collaborative, inclusive, and mission-driven culture that:
75. Encourages intellectual curiosity, experimentation, and continuous learning
76. Promotes psychological safety and healthy challenge
77. Rewards impact, rigor, and cross-functional partnership
78. Partner with HR and leadership on:
79. Hiring strategy and workforce planning for AI/ML roles
80. Career frameworks, competency models, and development pathways
81. Attract and retain top AI/ML talent by:
82. Providing compelling scientific challenges
83. Enabling visible impact on medicines for patients
84. Supporting opportunities for external engagement (conferences, publications, open-source where appropriate)
Why You? (Qualifications & Experience)
Basic Qualifications
85. Ph.D. in Computer Science, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Biophysics, or related quantitative discipline.
86. 12+ years of experience in the pharmaceutical, biotech, technology, or closely related industry, with at least 8 years in leadership roles managing multi-disciplinary AI/ML or computational science teams.
87. Demonstrated track record of:
88. Applying modern AI/ML methods (including deep learning and generative models) to complex biological, chemical, or healthcare problems
89. Deploying AI/ML solutions into production environments and achieving tangible impact on scientific or business outcomes.
90. Experience working with multiple data modalities (e.g., sequence, structure, images, omics, chemical structures, clinical/real-world data) and integrating them into AI/ML workflows.
Preferred Qualifications & Skills
91. A Transformational Leader
92. Proven ability to build new organizations or significantly reshape existing ones.
93. Experience unifying disparate teams into a cohesive, high-performance culture.
94. An AI/ML Visionary
95. Deep understanding of modern machine learning, including generative models, representation learning, and active learning.
96. Clear perspective on how these methods can be practically applied to discovery R&D and automation.
97. An Influential Collaborator
98. Exceptional ability to build alliances and communicate a compelling vision to stakeholders across science, engineering, and executive leadership.
99. Skilled at influencing without authority in a complex, matrixed environment.
100. A Scientific & Technical Driver
101. Passion for science and rigorous engineering, with a relentless focus on translating computational innovation into real-world medicines for patients.
102. Experience co-creating technology with end users and platform teams to drive adoption.
103. A Strategic Architect
104. Experience designing and implementing automated research frameworks, experiment-in-the-loop systems, or MLOps architectures is a plus.
105. A Global Leader
106. Experience managing distributed teams across geographies and cultures.
Why Join?
This is more than a leadership role; it is a mandate to build the applied AI/ML backbone of discovery. You will be empowered with the resources, talent, and executive support to create a truly next-generation discovery engine that works hand-in-hand with Discovery Data Sciences and the broader R&D ecosystem.
If you are a builder, a visionary, and a scientific leader driven to make a profound impact through applied AI/ML, we invite you to join us on this transformative journey.
Please visit 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.