Senior Research Engineer (Europe/UK – Remote)
Sword Health is shifting healthcare from human-first to AI-first through its AI Care platform, making world-class healthcare available anytime, anywhere, while significantly reducing costs for payers, self-insured employers, national health systems, and other healthcare organizations. Sword began by reinventing pain care with AI at its core, and has since expanded into women’s health, movement health, and more recently mental health. Since 2020, more than 700,000 members across three continents have completed 10 million AI sessions, helping Sword’s 1,000+ enterprise clients avoid over $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies and over 44 patents, Sword Health has raised more than $500 million from leading investors, including Khosla Ventures, General Catalyst, Transformation Capital, and Founders Fund. Learn more at www.swordhealth.com.
As a Senior Research Engineer on the AI Research team, you will serve as the engineering backbone within a group primarily composed of research scientists. You will be responsible for training production-ready models, deploying and maintaining them in production, and establishing strong engineering practices across the research organization. Our current roadmap is heavily focused on mental health, where the research team has been working the most. In this role, you will be critical to taking our models from research to production and ensuring they remain performant, safe, and up-to-date post-production, while bridging the gap between Research and Engineering. As Sword’s AI ambitions grow, this role may evolve to support other product areas across the company.
What you’ll be doing:
- Model Training & Deployment: Own the end-to-end lifecycle of production models, from training and fine-tuning (SFT, RLHF) to deployment, monitoring, and iterative updates in a live clinical environment;
- Production Model Maintenance: Monitor model performance post-launch, identify degradation or drift, and implement updates to keep models aligned with clinical requirements and evolving product needs;
- Engineering Excellence: Champion and establish software engineering best practices within the research team, including code quality, testing, reproducibility, and documentation, elevating the team’s production readiness;
- Bridge Research & Engineering: Act as the primary liaison between the AI Research and Engineering teams, ensuring research breakthroughs are translated into scalable, reliable product features;
- Data & Evaluation Pipelines: Build and maintain robust data pipelines and evaluation frameworks that power model training, testing, and continuous improvement.
What you need to have:
- PhD or MSc in a STEM field (preference for PhD in Computer Science);
- Research & Engineering Blend: 4+ years of professional experience spanning both ML research and engineering, with a demonstrated ability to take models from experimentation to production;
- Model Training Expertise: Hands-on experience with LLM fine-tuning, post-training techniques (SFT, RLHF), and model evaluation in production settings;
- Proficiency in Python and SQL (TypeScript is a plus) alongside modern software development best practices, including version control, testing, and CI/CD;
- Proven ability to introduce and uphold engineering standards within research-oriented teams, mentoring researchers on production best practices;
- Comfort with Ambiguity: The ability to thrive in the fast-moving chaos of a newly launched product, iterating rapidly on models and infrastructure that are not yet stable, while maintaining the highest quality and safety standards throughout;
- The ability to translate complex research concepts for engineering teams and vice versa, ensuring alignment across both functions ensuring alignment across both functions.
What we would love to see:
- Experience in fast-paced startup environments;
- Familiarity with cloud-native AI infrastructure (GCP/Vertex AI or AWS/SageMaker);
- Published research in ML, NLP, or related fields;
- Experience with LLM orchestration frameworks and agentic workflows.