Technical Program Manager, Enterprise

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

Technical Program Manager to partner with Frontier Agent Engineering teams on enterprise customer engagements, owning operational execution and delivery of technical work by managing timelines, milestones, risks, and dependencies. Drives strategic alignment and end-to-end execution of critical Enterprise initiatives, translating technical complexity into clear execution strategies and mitigating risks for reliable, high-value solutions at scale.

What you'd actually do

  1. Own the strategic planning, scheduling, and high-velocity execution of multiple enterprise-grade programs, ensuring on-time delivery against aggressive product goals. Run weekly cross-functional syncs, surface blockers, drive decisions.
  2. Manage complex dependencies and technical communication across core teams (e.g., Platform, Forward Deployed Engineering, Product) to seamlessly deliver frontier agents to our enterprise customers.
  3. Synthesize deep technical complexities into concise, actionable insights for both engineers and C-suite stakeholders. Drive absolute clarity across the delivery team regarding priorities, risks, and strategic outcomes.
  4. Proactively identify, track, and architect mitigations for technical risks unique to enterprise AI deployment, maintaining momentum in the face of ambiguity.
  5. Modernize and scale agile execution frameworks (e.g., Jira, Linear) to support rapid, iterative machine learning and software development lifecycles.
  6. Define, track, and report on key program health metrics, delivery forecasts, and engineering bottlenecks directly to executive leadership.

Skills

Required

  • 5+ years of experience as a Technical Program Manager or in a technical leadership role managing complex, large-scale software engineering or machine learning development projects.
  • 2+ years of dedicated experience managing programs focused directly on core engineering infrastructure, platform services, or distributed systems.
  • Strong foundational understanding of the Generative AI lifecycle, including LLM utilization for structured downstream tasks, model fine-tuning, and performance evaluation.
  • Proven track record of presenting to and influencing executive-level stakeholders, with the ability to translate complex technical challenges into clear business impacts.
  • Advanced proficiency with iterative development methodologies and modern project management tooling (Jira, Linear, etc.).

Nice to have

  • Strong software engineering fundamentals, ideally with prior professional experience as a software engineer or data developer before transitioning into program management.
  • Proven success driving the internal adoption of technical platforms, SDKs, or APIs across disparate product lines or independent business units.
  • Direct experience working with data quality pipelines, LLM-as-a-judge evaluation frameworks, or automated RLHF systems.

What the JD emphasized

  • enterprise customer engagements
  • technical leadership role
  • enterprise AI deployment
  • core engineering infrastructure, platform services, or distributed systems
  • Generative AI lifecycle
  • LLM utilization for structured downstream tasks
  • model fine-tuning
  • performance evaluation
  • data quality pipelines
  • LLM-as-a-judge evaluation frameworks
  • automated RLHF systems

Other signals

  • enterprise customer engagements
  • technical leadership role
  • program delivery from initial scoping to measurable, enterprise-wide adoption
  • systemic, measurable outcomes
  • full agentic application development process
  • hyper-growth, demanding AI environment
  • translate technical complexity into clear execution strategies
  • proactively mitigate risks
  • ensure our engineering teams deliver reliable, high-value solutions at scale