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New York, New York, United States
Hybrid
Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul.
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.
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
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
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
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice.
