swordhealth - Senior Research Engineer (Europe/UK - Remote)
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Requirements
• 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. • Sword Health complies with applicable Federal and State civil rights laws and does not discriminate on the basis of Age, Ancestry, Color, Citizenship, Gender, Gender expression, Gender identity, Gender information, Marital status, Medical condition, National origin, Physical or mental disability, Pregnancy, Race, Religion, Caste, Sexual orientation, and Veteran status.
Responsibilities
• 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.
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