What You'll Do
Lead the full lifecycle of AI models, from initial training and fine-tuning—using techniques like supervised fine-tuning and reinforcement learning with human feedback—to deployment, monitoring, and ongoing refinement in a live clinical environment. Ensure models remain accurate, safe, and responsive to evolving product and clinical needs.
Design and maintain data pipelines and evaluation systems that support continuous model improvement. Serve as the key technical link between research and engineering teams, translating experimental insights into robust, production-grade features.
Establish and promote engineering rigor within research workflows—driving standards in code quality, testing, reproducibility, and documentation to elevate the team’s ability to deliver production-ready solutions.
Requirements
- Advanced degree (PhD or MSc) in a STEM field, preferably Computer Science
- Minimum of 4 years of experience combining machine learning research and software engineering, with a track record of deploying models to production
- Hands-on expertise in fine-tuning large language models and applying post-training methods such as SFT and RLHF in real-world settings
- Strong proficiency in Python and SQL; familiarity with TypeScript is beneficial
- Experience with version control, automated testing, and CI/CD pipelines
- Ability to communicate complex technical concepts across research and engineering teams
- Proven success in mentoring research teams on engineering best practices and production workflows
- Comfort operating in fast-moving, uncertain environments while upholding high standards for quality and safety
Benefits
- Remote work flexibility with a preference for candidates in London, Lisbon, or Porto
- Opportunity to work across Europe and the UK in a globally distributed team
- Inclusive workplace committed to diversity and equity, with protections for all individuals regardless of age, gender, disability, race, religion, sexual orientation, veteran status, or other protected characteristics
