Senior, Forward Deployed Engineer (Physical AI & Self-Driving Labs)

Description

The Enterprise AI team at the company builds AI products that get used in production, working with scientific, clinical and business teams.

The company is a global biopharma business focused on getting ahead of disease through science, technology and talent. It aims to positively impact the health of 2.5 billion people by the end of the decade and works across respiratory, immunology and inflammation, oncology, HIV and infectious diseases.

The Senior Forward Deployed Engineer sits in the Chemical Manufacturing & Control (CMC) function, which turns a validated molecule into a reliably manufactured, quality-assured medicine. The function's current aim is to double New Product Introductions (NPIs).

You will be embedded with business unit teams to test whether AI ideas work, moving from proofs of concept, proofs of value and MVPs at a cadence of days to weeks. You will then hand proven solutions to the Sr. Solutions Engineer for scale-up. The role does not include carrying use cases into long-term operations.

The role builds Physical AI capabilities for self-driving labs and simulated tech transfers. You will connect digital and physical systems, simulating lab operation and line transfers through virtual commissioning and Omniverse representations to support higher throughput and one-click line deployment. You will work closely with automation, business and technology teams across R&D and manufacturing, and mentor engineers around you.

Most prototyping is AI-augmented. AI coding assistants and multi-agent workflows handle routine coding, so your effort goes into problem framing, judgement and validation. You will work inside the customer's business and deliver working software and a clear value hypothesis.

This is a UK-based hybrid role. Locations are Central London HQ or Stevenage, with a proposed move to Ware, UK by early 2029. Fully remote working is not available.

Responsibilities

• Own high-priority business unit use cases from problem framing to a working prototype, at a days-to-weeks pace, and form a clear view on whether each should continue.
• Ship first production versions yourself where the path is clear, before the Sr. Solutions Engineer takes them to scale.
• Run AI agent workflows covering planning, coding, testing, review and documentation for autonomous prototype delivery, choosing the right level of automation for each engagement.
• Use AI coding assistants as your default way of building, applying judgement, correcting course and validating output.
• Embed with business unit teams by attending standups, learning the domain and prototyping against real data and workflows.
• Package proven use cases for the Sr. Solutions Engineer, including working code, architecture rationale, open risks and scaling concerns.
• Start Code Orange, Responsible AI and Accountability Report discussions during prototyping so the productionisation team does not inherit governance gaps.
• After each engagement, contribute reusable agents, prompt templates and reference architectures to the organisation's pattern library.
• At the end of each engagement, make and defend continue or stop recommendations to business unit and Enterprise AI leadership.
• Coach business unit engineers on AI-augmented ways of working while you are embedded with them.

Requirements

• Strong full-stack or AI/ML engineering experience, including production deployments.
• Strong Python skills and proficiency in at least one of TypeScript/JavaScript, Go or similar.
• Hands-on experience with GenAI patterns, including agents, multi-agent orchestration, retrieval-augmented generation (RAG), prompt engineering and evaluation.
• Strong experience in physical settings such as labs, manufacturing control and edge deployment of models, including predictive and anomaly detection modelling and closed-loop control.
• Strong experience in lab automation and self-driving lab workflows, including robotics, instrument integration and digital-to-physical orchestration aligned with the business platform and workflow across scheduling, execution and data capture.
• Strong experience simulating manufacturing lines and lab operations on physics-based or 3D platforms such as NVIDIA Omniverse and Isaac Sim, applied to tech transfer, and familiarity with the NVIDIA physical AI and agentic model stack (Nemotron, etc.).
• Daily use of AI coding agents such as Claude Code, GitHub Copilot or Databricks AI Assistant as your main development mode.
• Preferred: prior experience as a Forward Deployed Engineer at an AI-native company (Anthropic, Palantir, OpenAI or similar), or as a Customer Engineer or Solutions Architect at a hyperscaler.
• Preferred: experience in regulated industries such as pharma, healthcare or financial services, with familiarity with GxP, GDPR and Sarbanes-Oxley (SOX).
• Preferred: experience with Manufacturing Execution Systems, Lab Execution Systems and similar.
• Preferred: experience extending lab automation into quality labs and manufacturing lines while working with automation, business and technology teams.
• Preferred: strong experience in computer vision, embedded systems and edge AI.
• Preferred: a record of contributing reusable engineering patterns back to a wider team.
• Bachelor's degree in Computer Science, Software Engineering or equivalent practical experience. A Master's degree in Computer Science or a related field is advantageous.
• Skills listed in the posting: AI frameworks, algorithm optimisation, artificial neural networks (ANNs), computer programming, deep learning, feature engineering, Google Cloud Platform (GCP) for machine learning, machine learning algorithms, machine learning operations, Microsoft Azure Data and AI, ML frameworks, model evaluation, model training, model validation, performance optimisation and the software development life cycle (SDLC).

Conditions

UK-based hybrid role with at least 2 to 3 days a week onsite. Fully remote working is not available.

Based in Central London HQ or temporarily in Stevenage, with the location moving to Ware, UK on a timeline set by the company, currently proposed for early 2029.

Closing date: 10 November 2026.

The company welcomes candidates from all backgrounds and underrepresented groups in technology, science and pharma.

Recruitment agencies may only refer candidates with prior written authorisation from the company; unauthorised referrals will not be paid.

US licensed healthcare professionals may have interview-related expenses captured and reported under US Transparency requirements.

Benefits

• Access to learning and development to build technical and leadership skills.
• A supportive team that values clear communication and practical problem solving.
• Work that supports technology used across the company and helps improve healthcare outcomes.