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Causaly

Knowledge-graph powered AI drug discovery

Senior AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listedHybrid

About the Job

Requirements

  • A master's degree or above in Computer Science, Electrical Engineering, or a related field.
  • At least 5 years of experience building AI/ML systems in production environments, including ownership of data collection, modeling, evaluation, deployment, and monitoring.
  • Proficiency in Python and modern machine-learning and agentic frameworks such as PyTorch, TensorFlow, or LangChain, with experience packaging models into APIs or integrating them into applications.
  • A solid understanding of large language models for natural language processing applications, including embeddings, prompt engineering, and fine-tuning.
  • Strong software engineering foundations, including version control, unit and integration testing, continuous integration and continuous delivery, and containerization, with a focus on reliability and scale.
  • Experience in product-focused teams, collaborating with designers, engineers, and product managers to scope and iteratively ship AI features.
  • Ability to reason about system behavior end-to-end, including model performance, latency, and observability and their impact on user experience.
  • Ability to document and defend architectural decisions and engage in thoughtful technical debate.

Responsibilities

  • Design and implement machine-learning and artificial-intelligence solutions end-to-end, from idea and data exploration through deployment and monitoring, balancing advanced techniques with pragmatism to deliver measurable impact.
  • Apply software engineering principles, including modularity, testing, code reviews, continuous integration and continuous delivery, and observability, to ensure AI systems are reliable, maintainable, production-ready, and adaptable.
  • Evaluate classical machine-learning and natural-language-processing techniques, large-language-model solutions, and agentic solutions to select approaches that balance speed, cost, complexity, interpretability, and performance.
  • Collaborate with product, design, and engineering teams to scope work, align on success metrics, and incrementally ship improvements in user-facing AI-powered features.
  • Document system architectures and decision rationale early and clearly to enable cross-team alignment and accelerate onboarding and iteration.
  • Champion model and data quality through dataset versioning, robust evaluation, fairness and bias assessment, and real-world performance tracking.
  • Mentor junior AI engineers and cross-functional teammates in modeling, coding, maintaining, and integrating product features, and help develop a high-trust, high-performance team culture.
  • Stay current with emerging research and tools, distill relevant insights, and bring innovations to the team to improve team capabilities and product opportunities.
  • Contribute to knowledge sharing through company-wide Slack channels, Show and Tell presentations, and technical deep dives.

Desired Qualifications

  • Experience with MLOps/LLMOps frameworks and best practices.
  • A PhD in Computer Science, Electrical Engineering, or a related field.
  • A background or work experience in life sciences, health technology, or other data-intensive domains.

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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