About the job
Established in 2018, Causaly is revolutionizing the realm of enterprise-level scientific research with our innovative AI platform. Our technology empowers researchers to efficiently discover, visualize, and interpret vital biomedical knowledge while automating essential research workflows, thus accelerating the solutions to some of the most pressing health challenges of our time.
We partner with leading biopharma companies and institutions, addressing diverse use cases in Drug Discovery, Safety, and Competitive Intelligence. For more insights on how we enhance knowledge acquisition and decision-making, explore our blog posts here: Blog - Causaly
Supported by prestigious venture capital firms including ICONIQ, Index Ventures, Pentech, and Marathon, we are on a mission to transform research outcomes in the biomedical field.
About the Team
We are seeking skilled AI engineers to join our dynamic team, utilizing generative AI and intelligent agents to help scientists uncover novel insights from extensive biomedical literature and datasets.
Delivering an accelerated research experience demands more than merely adopting the latest LLM model; it requires building trust with our users through integrating effective guardrails, biomedical curation, consistent updates, and robust deployment practices—ensuring our platform meets all the research needs of our users.
We invite AI engineers who are passionate about this mission to apply AI in reshaping how biomedical professionals conduct their research and daily explorations.
Your Responsibilities
· Design and implement comprehensive ML/AI solutions from conception through deployment and monitoring, balancing innovative techniques with practical considerations to achieve measurable outcomes.
· Employ sound software engineering practices, including modular design, testing, code reviews, CI/CD, and observability, to ensure AI systems are dependable, maintainable, and production-ready, while being adaptable to future needs.
· Select the optimal approach for each challenge, evaluating traditional ML and NLP techniques, LLM-based solutions, and agentic strategies to weigh trade-offs between speed, cost, complexity, interpretability, and performance.
· Collaborate closely with product, design, and engineering teams to define project scopes, establish success metrics, and iteratively enhance user-facing features powered by AI.

