ML Research Engineer
Circadia Health6 months ago
London, United KingdomMid Level
Responsibilities
- Research and develop machine learning models and algorithms for physiological foundation models, patient activity monitoring, radar-based bed-exit detection, and voice-based phenotyping.
- Review relevant ML research and prototype, adapt, and productionize ideas from the literature.
- Design and run rigorous, reproducible experiments with clear hypotheses and controlled comparisons.
- Implement and deploy efficient models in cloud infrastructure and on Circadia’s clinical monitoring hardware.
- Optimize models for constrained compute environments using techniques such as quantization, distillation, and efficient architectures.
- Collaborate with MLOps, backend engineering, clinical research, signal processing, and data teams on production requirements and validation studies.
- Define data collection requirements, performance benchmarks, and technical methods supporting clinical utility and regulatory approval.
- Document methods, results, and architectural decisions; present findings to technical and non-technical stakeholders.
- Contribute to publications, white papers, and regulatory submissions as needed.
Requirements
- A master’s degree in Computer Science, Machine Learning, Data Science, Mathematics, or another highly quantitative field.
- Production-grade, maintainable Python programming ability.
- Solid understanding of classical machine learning and experience applying it to real-world problems.
- Strong knowledge of deep learning methods and frameworks such as PyTorch, TensorFlow, or JAX.
- Ability to rapidly formulate, run, and learn from rigorous experiments and implement research papers in production-grade code.
- Strong written and oral communication skills for technical and non-technical audiences.
- Preferred qualifications include 3+ years of ML experience combining research and engineering, a PhD, cloud and production model deployment experience, healthcare or sensor-data experience, and evidence of exceptional competence through publications, open-source work, competitions, or hackathons.
Benefits
- Opportunity to work on real-world healthcare problems with measurable patient impact.
- Opportunity to build clinical-grade AI and ML systems and take ownership in a fast-growing, mission-driven company.
- Collaboration with a multidisciplinary team in a startup environment with high autonomy.
Categories
AI ResearchML Engineering