Founded in 2018, Causaly’s AI platform for enterprise‑scale scientific research redefines the limits of human productivity. We give humans a powerful new way to find, visualize and interpret biomedical knowledge and automate critical research workflows, accelerating solutions for some of the greatest health challenges we face.
We work with some of the world’s largest biopharma companies and institutions on use cases spanning Drug Discovery, Safety and Competitive Intelligence. You can read more about how we accelerate knowledge acquisition and improve decision making in our blog posts here: Blog - Causaly
We are backed by top VCs including ICONIQ, Index Ventures, Pentech and Marathon.
About the team
We’re hiring AI engineers to help us transform research outcomes in biomedical sciences. We utilise generative AI and agents to help scientists find novel connections and insights from biomedical literature and datasets.
Delivering a faster research experience is not just a matter of picking the latest LLM model. Getting accurate, scientifically relevant, trustworthy and meaningful results relies on building trust with our users that comes from integrating proper levels of guardrails, biomedical curation, consistent update cycles and robust deployment practices that make our platform a one‑stop shop for a researcher’s needs.
We’re looking for AI engineers who will champion this cause and help us apply AI to transform the way biomedical professionals carry out their research and day‑to‑day exploration.
What you’ll be doing
- Design and implement ML/AI solutions end‑to‑end, from the idea and data exploration phase to deployment and monitoring, balancing cutting‑edge techniques with pragmatism to deliver measurable impact.
- Apply strong software engineering principles, such as modularity, testing, code reviews, CI/CD and observability, to ensure AI systems are reliable, maintainable, production‑ready and can be readily adapted to future developments.
- Choose the right approach for the problem at hand, evaluating classical ML and NLP techniques, LLM‑based solutions, and agentic solutions to balance trade‑offs between speed, cost, complexity, interpretability, and performance.
- Collaborate closely with product, design, and other engineering teams to scope work, align on success metrics, and incrementally ship improvements in user‑facing features powered by AI.
- Document system architectures and decision rationale early and clearly, enabling alignment across teams and accelerating onboarding and iteration.
- Champion model and data quality, including dataset versioning, robust evaluation, fairness/bias assessment, and real‑world performance tracking.
- Mentor junior AI engineers and cross‑functional teammates, sharing best practices in modelling, coding, maintaining and integrating product features, and helping grow a high‑trust, high‑performance team culture.
- Stay up‑to‑date with emerging research and tools, distilling key insights and bringing back relevant innovations to elevate team capabilities and product opportunities.
- Contribute to a culture of knowledge sharing, through company‑wide Slack channels, Show and Tell presentations and technical deep‑dives.
What experience you’ll need to be successful
- A master’s degree or above in Computer Science, Electrical Engineering or a related field.
- 5+ years of experience building AI/ML systems in production environments, including ownership of key lifecycle stages: data collection, modeling, evaluation, deployment, and monitoring.
- Proficiency in Python and modern ML and agentic frameworks such as PyTorch, TensorFlow, or LangChain, with experience packaging models into APIs or integrating them into applications.
- A solid understanding of LLMs for natural language processing applications, including topics such as embeddings, prompt engineering and fine‑tuning.
- Strong software engineering foundations such as version control, unit/integration testing, CI/CD, containerization plus a mindset of building for reliability and scale.
- Experience working in product‑focused teams, collaborating with designers, engineers, and PMs, to scope and ship AI features iteratively.
- Ability to reason about system behavior end‑to‑end, including model performance, latency, and observability, and how these impact user experience.
- Clear, structured communicator, comfortable documenting and defending architectural decisions and engaging in thoughtful technical debate.
Not required, but it’s a plus if you also have:
- 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‑tech, or other data‑intensive domains
Benefits
- Competitive compensation package
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