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Causaly

Knowledge-graph powered AI drug discovery

Director of Application Engineering

Engineering LeadershipPosted on Nov 12
Full-timeLondon, UKNo salary listedHybrid

About the Job

Requirements

  • At least 10 years of software engineering experience and at least 4 years of experience leading engineering managers, ideally in a scale-up environment.
  • A track record of predictable delivery, including planning based on real capacity, limiting work in progress, and making trade-offs with Product.
  • A customer-first mindset focused on measuring teams by delivered customer value and bringing engineers close to the customers they build for.
  • Experience leading teams that build with AI coding agents, such as Claude Code, including adapting review, testing, and quality practices when agents write most of the code.
  • Experience leading distributed teams across countries and time zones.
  • Clear, direct communication with engineers, product leaders, and executives.
  • Broad engineering leadership across several sub-domains and the ability to strategically judge direction for areas outside one's personal depth.
  • Existing ability to embed AI in team workflows and advance AI-first workflows across a function, including shared tooling, standards, and measurable return.

Responsibilities

  • Lead the engineering teams responsible for the agentic research product and customer-facing applications through a group of engineering managers.
  • Set the direction for application engineering within the engineering strategy, define team structure and operating model, and make significant resourcing and structural decisions.
  • Develop monthly plans with Product based on real capacity, make trade-offs explicit as priorities change, ensure progress is visible, and drive roadmaps and cross-functional technical programmes.
  • Keep teams focused on customer outcomes, understand the problems customers' scientists are solving, assess whether shipped work is used and impactful, and change or stop work that is not effective.
  • Own the quality, security, and operational health of shipped products, including release practice and incident response.
  • Manage and coach engineering managers, build a strong management layer, own the hiring plan, and be accountable for organizational culture.
  • Drive AI-first workflows across teams, invest in reusable tooling and enablement, and set standards and return-on-investment targets for using agents from planning through code review and release.
  • Partner with VPs and Product leadership on what to build, coordinate with Data, DevOps, and Security on how it fits together, and represent engineering externally in hiring.
  • Participate in the engineering leadership team, shape engineering-wide decisions, and step in to lead engineering when needed.
  • Ensure teams plan against real capacity with Product and deliver predictably in small increments.
  • Ensure each area has a clear owner and teams are structured around clear mandates.
  • Establish AI-first engineering practices across the function, including planning, automated testing, review, and release.
  • Develop engineering managers and create teams that attract and retain strong engineers.
  • Ensure teams judge success by customer value rather than feature volume and that customers notice improvements with each release.

Desired Qualifications

  • Experience leading teams that ship large language model, machine learning, or other AI-powered features to production, with enough technical depth to challenge architecture and quality decisions.
  • Experience in life sciences, biomedical data, or business-to-business software as a service for enterprise customers.
  • Hands-on experience building agentic systems, including multi-step agents, tool use, and evaluation.

About the Company

Causaly

Knowledge-graph powered AI drug discovery

Causaly builds an agentic AI research platform for life sciences R&D that helps drug discovery and clinical development teams find and interpret biomedical evidence, turning research questions into cited, reviewable answers. It blends a cause‑and‑effect biomedical knowledge graph with scientific information retrieval and a data fabric that links public literature, full‑text journals, and a customer’s internal data; its products include Agentic Research, Scientific Workflows, Bio Graph and API, and Pipeline Graph, with integration into Microsoft 365 Copilot. The platform differentiates itself by using a deep, agentic AI approach and a unified data fabric to connect external literature with internal data for end‑to‑end research workflows. Its goal is to accelerate life sciences R&D by turning vast biomedical information into actionable, citable evidence that speeds up decision making and validation in drug discovery and clinical development.

Company size
51-200
Company stage
Series B
Total funding
$82.7M
Headquarters
London, United Kingdom
Founded
2018

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