AI Research Scientist (Europe/UK - Remote)
Sword Health
At Sword, we operate as both a clinical-centric frontier AI lab and an applied AI platform, conducting the foundational research that makes clinical AI possible, then putting it into practice through platforms that treat patients directly. Our AI Research team builds the core intelligence behind Dawn and Phoenix, our continuous, always-on care agent.
Healthcare demands AI that goes far beyond answering questions or completing tasks. It requires systems capable of sustained engagement across dozens of interactions, understanding a patient's history, perceiving their present state, and guiding their recovery over weeks and months. To make this possible, we are pushing the boundaries of memory systems, multimodal perception (video, audio, wearable signals), and multi-turn reinforcement learning for long-horizon treatment planning.
We are building a category of AI product that has never existed before, AI that provides real clinical care autonomously, at scale. That means operating at the frontier of both research and product development simultaneously, often without a playbook. You will thrive here if you are energized by uncertainty, comfortable making high-stakes decisions with incomplete information, and motivated by the outsized impact that comes with pioneering something genuinely new. It's high pressure, high reward — and the team that does it best.
Our researchers don't choose between scientific rigor and product impact. The team actively publishes in top-tier AI conferences and clinical journals while shipping the models that power care for hundreds of thousands of patients. We have the compute, the data, and the team to support your best work.
What you'll be doing:
- Design and execute research on LLM fine-tuning, alignment, and post-training methods (SFT, RLHF) tailored for clinical and therapeutic domains;
- Develop and improve foundational AI models that power our AI agents, spanning language, vision, speech, and multimodal systems;
- Contribute to the full model development cycle: dataset curation and annotation, architecture design, training, evaluation, and iteration;
- Collaborate across AI Engineering, Product, and Clinical teams to translate research breakthroughs into production systems that deliver patient care;
- Work towards long-term ambitious research goals, such as clinical memory, long-horizon planning, and safety validation, while identifying and delivering immediate milestones;
- Advance the field by publishing in top-tier AI venues and clinical journals, contributing to Sword's growing body of peer-reviewed research.
What you need to have:
A PhD in Computer Science, Machine Learning, Natural Language Processing, or a closely related AI field;
- Hands-on experience fine-tuning large language models (pre-training, SFT, RLHF, or related post-training techniques);
- A strong publication track record in peer-reviewed AI conferences or journals;
- Proficiency in Python and deep experience with modern ML frameworks (e.g., PyTorch, JAX);
- Demonstrated ability to design rigorous experiments and interpret their results.
What we would love to see:
- First-author publications in top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, CVPR);
- Deep expertise in one or more of: large language models, reinforcement learning from human feedback, multimodal learning (vision, speech), or agentic AI systems;
- Experience building or contributing to LLM-based agents, including prompt engineering, memory orchestration, or agentic workflows;
- A track record of taking research ideas from conception to working systems, including developing and debugging complex ML pipelines;
- Industry experience during or after the PhD (e.g., research internships at leading AI labs);
- Comfort with ambiguity and a track record of delivering results in fast-moving, high-uncertainty environments where research and product development happen in parallel;
- Strong communication skills and a history of effective cross-functional collaboration;
- A broader record of research excellence demonstrated through grants, fellowships, patents, or impactful open-source contributions.