Forward Deployed Engineer

Posted 04 September 2026
LocationUnited Kingdom
Discipline AI, Machine Learning & Deep Learning
Reference35183

Job description

Forward Deployed Engineer β€” Stealth-mode AI Startup, Life Sciences
πŸ“ SF/Bay Area or NYC preferred (open to remote, with periodic travel)
πŸ’° $140–200K base | ~$300K total comp with equity

A fast-growing, well-funded early-stage startup (~30 people, just past seed) is building fully AI-native products, including conversational voice/video products, for the pharma industry to help commercial teams engage doctors, support patients, and streamline field enablement. They're already working with several top-10 global pharma companies and are scaling their NYC presence alongside their Bay Area HQ.

They're hiring a Forward Deployed Engineer to sit at the intersection of AI engineering, deployment, and direct client work. This is someone who can build production AI applications and be the technical face of the company with enterprise clients.

What you'd do:

  • Build and ship AI-powered applications using LLMs and modern AI frameworks

  • Design and deploy production infrastructure (cloud-agnostic, AWS preferred)

  • Work directly with enterprise clients to scope, customize, and implement solutions

  • Own a significant piece of product/deployment from day one

What they're looking for:

  • Experience in software engineering and building AI products/agents 

  • Python & Typescript (AWS preferred)

  • Real experience building with LLMs/AI agents (side projects count)

  • Experience shipping something to production, on any cloud

  • Excellent communication skills (this is a highly client-facing role)

  • Startup DNA preferred (comfort with ambiguity, high ownership); big tech/consulting backgrounds welcome if you’re hungry to join a startup 

  • Pharma/regulated industry experience is a nice-to-have, not required

Why it's interesting:

  • Ground-floor opportunity β€” you'd help shape not just the product but the culture

  • Regular exposure to Director/VP-level stakeholders at major pharma companies

  • Solving genuinely hard, novel AI problems (e.g., voice agents that need to correctly pronounce drug names)

  • High-ownership, high-transparency environment