Director, Forward Deployed Engineering

Scale AI Scale AI · Data AI · London, United Kingdom · Enterprise Engineering

Director of Forward Deployed Engineering at Scale AI, leading a team to deliver and integrate AI agents for large enterprise customers. The role involves owning end-to-end delivery, technical oversight of agents, models, evaluations, and infrastructure, and partnering with Product teams to translate field lessons into reusable platform capabilities. Requires strong leadership, hands-on AI stack fluency, and experience deploying AI in complex enterprise environments.

What you'd actually do

  1. Own delivery for our largest accounts.
  2. Lead and grow the team.
  3. Provide holistic technical oversight.
  4. Partner with Product and core Engineering.
  5. Engage directly with customer leadership.

Skills

Required

  • Leadership of customer-facing engineering teams
  • Technical delivery experience
  • Software engineering background
  • System architecture
  • Code review
  • Production debugging
  • Team scaling (hiring, onboarding, standards)
  • Full AI stack fluency
  • LLMs and agent systems
  • Evaluation frameworks
  • Backend and API design (Python or TypeScript)
  • Data systems (PostgreSQL, DynamoDB, MongoDB, or similar)
  • Cloud infrastructure (AWS, Azure, or GCP)
  • Kubernetes, Docker, IaC tooling
  • Deploying AI in complex enterprise environments
  • Navigating security reviews, identity/SSO, data residency, legacy integrations, and compliance requirements
  • Translating field work into product leverage
  • Cross-functional leadership
  • Communication with executive and non-technical audiences

Nice to have

  • Founder mindset
  • Customer empathy

What the JD emphasized

  • deeply integrated AI agents
  • agent design and evaluation
  • backend services, data systems, and cloud infrastructure
  • scale a team that can operate under pressure
  • ship
  • durably those deployments stick
  • sharpen our product roadmap
  • high-autonomy, founder-mindset role
  • own outcomes end-to-end
  • scale a team
  • operate under pressure
  • shape how AI adoption actually happens
  • how consistently your team ships
  • how durably those deployments stick
  • how clearly the signal from the field sharpens our product roadmap
  • ship efficiently
  • reusable platform capabilities
  • compound our velocity
  • founder mindset
  • own outcomes without waiting to be asked
  • move with urgency in ambiguous environments
  • make sound calls under pressure
  • model the calm, focus, and judgment the team needs when stakes are high
  • 10+ years of software engineering or technical delivery experience
  • 3+ years leading high-performing customer-facing engineering teams
  • led high-pressure technical projects from prototype to production
  • still comfortable whiteboarding system architectures, reviewing code, and debugging production issues
  • built hiring bars, onboarding programs, and operating standards for technical organizations
  • interviewed and hired at volume for senior engineering roles
  • Hands-on fluency with the full AI stack
  • Deep familiarity with LLMs and agent systems, evaluation frameworks, backend and API design (Python or TypeScript), data systems (PostgreSQL, DynamoDB, MongoDB, or similar), and cloud infrastructure (AWS, Azure, or GCP) including Kubernetes, Docker, and IaC tooling
  • Experience deploying AI into complex enterprise environments
  • navigating security reviews, identity/SSO, data residency, legacy integrations, and compliance requirements
  • track record of turning field work into product leverage
  • codified repeatable patterns from customer engagements into tools, libraries, or platform features
  • scaled across the next wave of deployments
  • Strong cross-functional leadership
  • aligned Sales, Product, Engineering, Research, and Customer Success around shared goals
  • delivered outcomes that required all of them to row in the same direction
  • Founder mindset
  • High autonomy, high motivation, high ownership
  • Comfortable operating with ambiguity, bias toward action
  • judgment to know when to ship and when to raise the bar
  • Exceptional communication
  • translate technical tradeoffs into decisions for executive and non-technical audiences
  • model the customer empathy you expect from your team

Other signals

  • leading customer delivery of AI agents
  • shipping deeply integrated AI agents
  • turning research breakthroughs into production systems
  • driving AI adoption inside enterprises
  • scaling a team that operates under pressure