Director, Model Research – Development

Thomson Reuters

. Provide hands-on technical direction for the model research program .

Posted 10/6/2026full-timeZug • SwitzerlandLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates hands-on expertise in supervised fine-tuning, preference optimization, and reinforcement learning, with a strong focus on data selection and evaluation design. Proven ability to lead technical teams and collaborate on research publications in regulated professional domains.

Highest-signal resume keywords

Supervised Fine-TuningReinforcement LearningData SelectionDistributed TrainingModel Safety

ATS Keywords

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Hard Skills

Preference OptimizationSynthetic-Data GenerationEvaluation DesignTraining Pipeline ManagementModel Behavior AnalysisMulti-Step RLContinued Pre-TrainingDomain AdaptationAdversarial RobustnessShipped Models

Soft Skills

CollaborationLeadership

Tools & Technologies

Data PipelinesEvaluation Infrastructure

Industry Keywords

LLMsRegulated Professional DomainNeurIPSICMLACLEMNLP

About the role

Key responsibilities & impact

Provide hands-on technical direction for the model research program
Lead the evolution of post-training and data strategy for accurate, calibrated, and trustworthy professional-workflow models
Own hands-on post-training execution for Thomson’s LLMs, including supervised fine-tuning, preference optimization, and reinforcement learning
Stand up and run online, agentic reinforcement-learning pipelines with subject-matter experts in the loop
Own data selection, mixture optimization, synthetic-data generation, and evaluation design
Monitor training runs, identify problems before completion, and determine corrective actions
Work day-to-day with infrastructure and evaluation teams to maintain reliable training and evaluation pipelines
Bring findings and recommendations forward to inform roadmap and prioritization decisions
Collaborate on experiments, training and evaluation pipelines, and research publications at top-tier conferences
Report to the Vice President of Model Research & Development

Requirements

What you’ll need

Hands-on command of supervised fine-tuning, preference optimization (e.g., DPO), and reinforcement learning, including agentic, multi-step RL where models learn to use tools and complete tasks
Personal experience shaping data selection, mixture design, synthetic-data generation, and evaluation design, with measured effects on model behavior
Hands-on command of distributed training, data pipelines, and evaluation infrastructure; able to build and debug these systems
Track record of shipped models, widely-used open-source contributions, or peer-reviewed publications at top research venues such as NeurIPS, ICML, ACL, or EMNLP
Experience leading a focused technical team in training and/or evaluation
Experience applying LLMs in law, tax, or another comparably regulated professional domain (preferred)
Experience with continued pre-training and domain adaptation of large base models (preferred)
Experience with model safety, adversarial robustness, and red-teaming (preferred)

Benefits

Comp & perks

Flexible hybrid working environment for office-based roles
Work from anywhere for up to 8 weeks per year
Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Two paid volunteer days off annually
Opportunities to participate in pro-bono consulting projects and ESG initiatives
Career development, continuous learning, and skills development through Grow My Way programming
Flexible workplace policies supporting work-life balance
Employee inclusion, belonging, and social impact programs