Senior Forward Deployed Engineer; Physical Ai & Self-Driving Labs

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, and we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma.

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 sits 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 will 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 partnering with the Sr. Solutions Engineer is part of the role.

This role will build Physical AI capabilities to create self-driving labs and simulated tech transfers. You will work on orchestration of the physical space from the digital space. You will simulate the functioning of labs and a transfer to a line via virtual commissioning and Omniverse representations. This will support the ambitions of higher throughput and one-click deployment of a line.

You will work hand in hand with the automation business and tech teams across R&D and manufacturing. You bring deep, hands-on AI/engineering expertise in Physical AI and the judgement to turn ambiguous technical problems into shipped, production-grade systems, while mentoring the engineers around you.

• 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.
• 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.
• 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, including working code, architecture rationale, open risks, and scaling concerns.
• 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.

We are seeking professionals with the following required skills and qualifications:

• Strong experience of full-stack or AI/ML engineering, including production deployments.
• Strong…