Research Engineer, Domain Scaling

The Role

The Domain Scaling team has the goal of making Normal’s Agents world-class at anything Chip-Engineering and EDA-related, UVM, debugging, analog, lean formalization, materials-aware optimization, etc. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models.

You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.

What You Will Own

• Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
• Build and manage relationships with external vendors, including outreach, evaluation of data quality, and reward design
• Collaborate with domain experts to design data pipelines and evaluations
• Explore novel ways of creating RL environments for high-value tasks
• Develop and improve QA frameworks to catch reward hacking and ensure environment quality
• Run generalization experiments to measure how data strategy changes improve model capabilities
• Partner with other AI researchers and product teams to translate capability goals into training environments, evals, and real product features

What Makes You a Great Fit

• Have experience with post-training large language models for specific domains or real-world use cases
• Have experience with reinforcement learning, reward design, or training data curation for LLMs
• Are comfortable managing technical vendor relationships and iterating quickly on feedback
• Find value in reading through datasets to understand them and spot issues
• Have strong cross-functional collaboration skills
• Are passionate about making AI more useful for chip development and recursive hardware self-improvement
• Are excited about a role that includes a combination of applied research and hands-on data work

Bonus Points

• Have experience training production ML systems
• Have experience designing evals or benchmarks for LLMs
• Have domain expertise in a vertical where we would like to make our models more useful
• Have experience working with external vendors or technical partners