Established in 2018, Causaly is at the forefront of transforming enterprise-level scientific research through its innovative AI platform. Our technology empowers researchers to efficiently discover, visualize, and interpret extensive biomedical knowledge while automating essential research workflows, thereby hastening the development of solutions for critical health challenges.We collaborate with some of the largest biopharma companies and prestigious institutions, addressing diverse use cases that encompass Drug Discovery, Safety, and Competitive Intelligence. To learn more about how we enhance knowledge acquisition and decision-making processes, visit our blog: Blog - Causaly.We are proudly supported by renowned venture capital firms including ICONIQ, Index Ventures, Pentech, and Marathon.About the TeamWe are seeking talented AI Engineers to join our mission of revolutionizing research outcomes in biomedical sciences. Our team leverages generative AI and intelligent agents to assist scientists in uncovering novel insights and connections from biomedical literature and datasets.Enhancing the research experience is more than just adopting the latest LLM model; it requires building trust with our users through the integration of effective guardrails, rigorous biomedical curation, consistent update cycles, and robust deployment practices. This approach ensures that our platform serves as a comprehensive resource for researchers.We are in search of AI Engineers who are passionate about applying AI to transform how biomedical professionals conduct their research and daily inquiries.Your Responsibilities· Design and implement end-to-end ML/AI solutions, from conceptualization and data exploration to deployment and monitoring, striking a balance between cutting-edge techniques and practical application to deliver tangible results.· Employ strong software engineering principles such as modularity, testing, code reviews, CI/CD, and observability to ensure AI systems are dependable, maintainable, production-ready, and adaptable to future advancements.· Evaluate various approaches including classical ML, NLP techniques, LLM-based solutions, and agentic solutions to determine the best fit for the problem at hand, considering trade-offs related to speed, cost, complexity, interpretability, and performance.· Collaborate closely with product, design, and engineering teams to define project scope, align on success metrics, and iteratively enhance user-facing features powered by AI.
Mar 4, 2026