Senior, Forward Deployed Engineer (Digital Twin & Optimization)

Job description

Senior, Forward Deployed Engineer (Digital Twin & Optimization) About GSK Enterprise AI Enterprise AI is GSK’s centre of gravity for applied artificial intelligence. We pair forward deployed engineers and full-stack engineers with scientific, clinical, and business teams to build AI products that get used — not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients. GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team — we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma. Position Summary GSK’s Enterprise AI team builds AI solutions that prove value fast and scale to production. The Forward Deployed Engineer (FDE) is the engineer GSK sends in to find out whether an AI idea will work — embedded with business unit (BU) teams, proving ideas through proofs of concept (PoCs), proofs of value (PoVs), and minimum viable products (MVPs) at a days-to-weeks cadence, then handing the proven solution off to the Sr. Solutions Engineer for scale-up. The pattern is proven by AI-native companies — Anthropic, Palantir, OpenAI — and this role brings that model to GSK. This specific role sites in the Chemical Manufacturing & Control (CMC) area. This is the GSK function that turns a validated molecule into a reliably manufactured, quality-assured medicine; its mission is to get safe, effective, consistently manufactured medicines to patients, faster. Its current mission is to double New Product Introductions (NPIs). As a Senior Forward Deployed Engineer, you’ll be embedded in a team shaping how AI transforms GSK’s process development around the world. Most prototyping work is AI-augmented: AI coding assistants and multi-agent workflows handle the bulk of routine coding, freeing the FDE to focus on framing, judgement, and validation. The role works directly inside the customer’s business and emerges with working software and a defensible value hypothesis, not slideware. The FDE does not carry use cases through to long-term operations and partner with the Sr. Solutions Engineer is part of the role. This role will build in-silico process models and digital twin capabilities to foster the process development of molecules. You will work on foundation models, platform fitness models and tech transfer twins, plus scenario planning and scheduling optimization. You will still build models yourself, with a platform mindset: platform models and end-to-end solutions, not point solutions. Process modelers remain the process experts and co-own the models. You bring the AI modeling and engineering that makes building, deploying and using models 10x more efficient. You bring deep, hands-on AI/engineering expertise and the judgement to turn ambiguous technical problems into shipped, production-grade systems, while mentoring the engineers around you. Key Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: PoC / PoV / MVP Delivery Take ownership of high-priority BU use cases from problem framing through to a working prototype, at days-to-weeks cadence, and form a defensible view on whether each should progress. Ship first production versions directly for use cases where the path is clear — before the Sr. Solutions Engineer takes them forward to scale. AI-Native Engineering Operate AI agent workflows (planning, coding, testing, review, documentation) for autonomous prototype delivery — selecting the right level of automation for each engagement. Use AI coding assistants as the default mode of building; provide judgement, course correction, and validation. Customer Embedding & Handoff Embed with BU teams — attend standups, understand the domain, prototype against real data and real workflows. Package proven use cases cleanly for the Sr. Solutions Engineer — working code, architecture rationale, open risks, and scaling concerns. Governance, Standards & Coaching Begin Code Orange, Responsible AI, and Accountability Report conversations during prototyping — so the productionisation team does not inherit governance debt. Contribute reusable agents, prompt templates, and reference architectures to the organisation’s pattern library after each engagement. Form and defend kill/continue recommendations at the end of each engagement — presenting clearly to BU and Enterprise AI leadership. Coach BU engineers on AI-augmented ways of working while embedded, building BU capability alongside delivery outcomes. Why You! We are seeking professionals with the following required skills and qualifications to help us achieve our goals: Strong full-stack or AI/ML engineering experience, including production deployments. Strong proficiency in Python and at least one of TypeScript/JavaScript, Go, or similar. Strong awareness of the main process modeling approaches (mechanistic and first-principles models, hybrid models, data-driven and ML models, and process simulation), enough to work with process modelers, understand what each approach needs, and spot where AI can make it 10x more efficient. Deep experience in building mechanistic models is not expected. Strong AI modeling skills, including model identification: identifying, selecting and calibrating the right model structure from process data, for example with surrogate models, AutoML and agents that assist modelers. Proven ability, as a forward deployed engineer, to take a digital twin or optimization MVP end to end, from problem framing to a working, deployed and commercial-ready solution with the scientists and modelers. This role does not own an ML engineering platform. Strong experience in optimization and scenario planning: linear and mixed-integer programming, constraint solvers, scheduling optimization, and simulation-based what-if analysis. Hands-on experience with GenAI patterns (agents, multi-agent orchestration, evaluation) and enough machine learning operations (MLOps) and model serving to ship an MVP to production. Daily use of AI coding agents (Claude Code, GitHub Copilot, Databricks AI Assistant) as the primary mode of development. Preferred Experience Prior experience as a Forward Deployed Engineer at an AI-native company (Anthropic, Palantir, OpenAI, or similar), or as a Customer Engineer / Solutions Architect at a hyperscaler. Experience with digital twins, surrogate or foundation models, and platform or fitness models for pharmaceutical drug substance or drug product processes. Experience partnering with data analytics, data science, or process science teams to turn analytical models into shared, reusable products. Experience in regulated industries (pharma, healthcare, financial services) and familiarity with good practice regulations (GxP), General Data Protection Regulation (GDPR), and Sarbanes-Oxley (SOX). Track record of contributing reusable engineering patterns back to a broader team. Education Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience. Master’s degree in Computer Science or related field is advantageous. Ready to apply? If this role excites you and matches your experience, we would love to hear from you. Please submit your application and a short note telling us why you want to join and what you would bring to this role. We look forward to hearing from you. If you need adjustments during the process, tell us and we will support you. (Please take a personal copy of the Job Description, as this will not be available online post closure of the advert) Closing Date:   10th November 2026 Working Pattern This is a UK based hybrid role, with a min of 2/3 days a week onsite. Fully remote working is not available. Locations: 1) HQ Central London or 2) Stevenage - This role can be temporarily be based