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Director, Model Research and Development — Zug, Switzerland (Hybrid)
Lead Thomson Reuters’ LLM post-training, reinforcement learning, and data strategy. Develop reliable AI models for legal, tax, and other regulated professional workflows.
विवरण
- Provide technical leadership for the model research program
- Advance post-training and data strategies for accurate, calibrated, and trustworthy models used in professional workflows
- Direct hands-on post-training of Thomson Reuters’ LLMs through supervised fine-tuning, preference optimization, and reinforcement learning
- Build and operate online, agentic reinforcement-learning pipelines with subject-matter experts involved
- Direct data selection, mixture optimization, synthetic-data creation, and evaluation design
- Track training runs, detect issues early, and define appropriate corrective measures
- Partner daily with infrastructure and evaluation teams to sustain dependable training and assessment pipelines
- Present research findings and recommendations to guide roadmap and prioritization decisions
- Contribute to experiments, training and evaluation pipelines, and research papers for leading conferences
- Report to the Vice President of Model Research & Development
आवश्यकताएँ
- Demonstrated expertise in supervised fine-tuning, preference optimization such as DPO, and reinforcement learning, including agentic multi-step reinforcement learning for tool use and task completion
- Practical experience defining data selection, mixture design, synthetic-data generation, and evaluation approaches, with measured impact on model behavior
- Strong hands-on expertise in distributed training, data pipelines, and evaluation infrastructure, including the ability to build and troubleshoot these systems
- Record of shipped models, broadly adopted open-source work, or peer-reviewed publications at leading venues such as NeurIPS, ICML, ACL, or EMNLP
- Experience leading a focused technical team responsible for training and/or evaluation
- Experience applying LLMs in law, tax, or a similarly regulated professional field preferred
- Experience with continued pre-training and domain adaptation for large base models preferred
- Experience in model safety, adversarial robustness, and red-teaming preferred
लाभ
- Flexible hybrid work arrangements for office-based roles
- Up to eight weeks of working from anywhere each year
- Flexible vacation policy
- Two company-wide Mental Health Days annually
- Access to the Headspace app
- Retirement savings program
- Tuition reimbursement
- Employee incentive programs
- Resources supporting mental, physical, and financial wellbeing
- Two paid volunteer days each year
- Opportunities to join pro bono consulting projects and ESG initiatives
- Career development, ongoing learning, and skills training through Grow My Way
- Flexible workplace policies designed to support work-life balance
- Employee inclusion, belonging, and social impact programs
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