Job Description

Site Name:

USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage

Posted Date:

Dec 11 2025

We are seeking a dynamic scientific and technical leader to build and direct a new

Discovery

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 which owns the core predictive modeling, analytics, and data science support across modalities, Discovery Applied AI/ML will:

Focus on

AI/ML innovation, engineering, and productization

(e.g., generative design tools, active learning loops, LIAL frameworks) Rapidly translate emerging AI/ML methods and technologies into

robust, deployed solutions

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

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. Establish and clearly articulate a vision for a

research- and service-oriented applied AI/ML organization

focused on: Creation, evaluation, and deployment of

state-of-the-art

AI/ML techniques and platforms

Direct enablement of automated discovery paradigms, including

Lab-in-an-Automated-Loop (LIAL)

and other closed-loop experimentation systems Design and implement an organizational model that integrates:

Applied ML research

(novel architectures, generative models, active learning)

AI/ML engineering &

platformization

(scalable services, APIs, reusable components)

AI product management

(use-case discovery, user-centric design, adoption) Develop a

multi-year strategic roadmap

for how applied AI/ML will: Increase the Probability of Technical and Regulatory Success (PTRS) Shorten design-make-test-analyze cycles Enhance decision quality across discovery programs

2. Portfolio Impact & Scientific Partnership

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. Work in

tight coordination with Discovery Data Sciences

to: Identify high-impact problems where AI/ML can drive step-change improvements Decide when to advance from prototype to platform Ensure clear delineation between core data science support (DDS) and advanced/applied AI/ML builds (DAI/ML). Lead

problem-framing and solution design

for AI/ML use cases: Generative design (molecules, proteins, biologics, modalities) Active learning and optimization in high-throughput screening AI-guided experiment planning and lab scheduling Multi-modal integration for mechanism-of-action and target/context selection Establish and maintain

stage-gated, fail-fast frameworks

for AI/ML projects: Clear hypotheses Success metrics tied to scientific or operational outcomes Criteria for scale-up, sunset, or pivot Communicate results and impact effectively to diverse audiences: Scientific stakeholders (detailed methods, data, and models) Platform & engineering teams (interfaces, requirements, performance) Executives (value, risk, investment needed, portfolio impact)

3. AI/ML Innovation, Engineering & Research Leadership

Drive a

culture of pioneering applied AI/ML research

, with emphasis on: Generative models (e.g., diffusion models, VAEs, transformers) for molecular and protein design Active learning, Bayesian optimization, reinforcement learning for closed-loop experimentation Foundation models and large-scale representation learning across biological, chemical, and omics data Multi-modal integration (e.g., sequence, structure, imaging, omics, real-world data) Allocate

protected time

and resources for your team to: Explore emerging methods Run exploratory pilots with clear transition criteria Contribute to publications, preprints, and community engagement (as appropriate) Ensure that promising methods are

translated into robust, maintainable solutions

: Collaborate with engineering and platform teams to build scalable APIs, services, and tools Establish best practices for model lifecycle management (MLOps) in partnership with R&D Digital & Tech Implement reproducible and compliant workflows for model development, validation, and monitoring Champion

ethical, transparent, and compliant

AI/ML: Ensure appropriate safeguards, interpretability, and documentation Work with Risk & Compliance to align with regulatory and internal governance requirements

4. Platform & Technology Build Leadership

Partner with

Discovery Data Sciences, Discovery Engineering & Integration, Automation, Cheminformatics, Protein Design & Informatics

, and R&D Digital & Tech to: Architect and deliver

AI-augmented platforms

for design, analysis, and decision support Enable

LIAL and automated discovery frameworks

, where AI/ML models actively inform experiment selection and optimization Co-lead

priority technology builds

, ensuring: AI/ML capabilities are designed as reusable components and services Seamless integration with data platforms (e.g., Onyx, QEL) and lab automation systems Alignment with enterprise standards for data, APIs, security, and compliance Define and track

technical and business KPIs

for AI/ML systems: Model performance and robustness Usage and adoption metrics Impact on cycle times, cost, and decision quality

5. Thought Partnership & Internal Advocacy for AI/ML

Serve as a

trusted thought partner

to Discovery leadership on AI/ML: Help shape the AI/ML aspects of discovery strategy Advise on where to buy, build, or partner for AI/ML capabilities In collaboration with the technology evaluation / innovation roles, continuously scan the

external AI/ML landscape

: Evaluate emerging tools, platforms, and models for applicability Recommend strategic collaborations or partnerships where they can accelerate impact Provide

training, education, and evangelism

: Help non-ML experts understand what AI/ML can and cannot do Develop materials, seminars, and office hours for scientists and leaders

6. Talent & Culture Development

Build, lead, and mentor

a high-performing global team of: Applied ML scientists ML/AI engineers AI product managers / technical program leads Foster a

collaborative, inclusive, and mission-driven culture

that: Encourages intellectual curiosity, experimentation, and continuous learning Promotes psychological safety and healthy challenge Rewards impact, rigor, and cross-functional partnership Partner with HR and leadership on:

Hiring strategy and workforce planning

for AI/ML roles Career frameworks, competency models, and development pathways Attract and retain top AI/ML talent by: Providing compelling scientific challenges Enabling visible impact on medicines for patients Supporting opportunities for external engagement (conferences, publications, open-source where appropriate)

Why You? (Qualifications & Experience)

Basic Qualifications

Ph.D. in

Computer Science, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Biophysics

, or related quantitative discipline.

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. Demonstrated track record of: Applying modern AI/ML methods (including deep learning and generative models) to

complex biological, chemical, or healthcare problems

Deploying

AI/ML solutions into production environments and achieving

tangible

impact

on scientific or business outcomes. 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

A Transformational Leader

Proven ability to build new organizations or significantly reshape existing ones. Experience unifying disparate teams into a cohesive, high-performance culture.

An AI/ML Visionary

Deep understanding of modern machine learning, including generative models, representation learning, and active learning. Clear perspective on how these methods can be practically applied to discovery R&D and automation.

An Influential Collaborator

Exceptional ability to build alliances and communicate a compelling vision to stakeholders across science, engineering, and executive leadership. Skilled at influencing without authority in a complex, matrixed environment.

A Scientific & Technical Driver

Passion for science and rigorous engineering, with a relentless focus on translating computational innovation into

real-world medicines

for patients. Experience co-creating technology with end users and platform teams to drive adoption.

A Strategic Architect

Experience designing and implementing

automated research frameworks

, experiment-in-the-loop systems, or MLOps architectures is a plus.

A Global Leader

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 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.


Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities contact us at HR.AmericasSC-CS@gsk.com where you can also request a call.


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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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