Research Engineer, Domain Scaling

Drive Capital; København; On-site; DKK 900,000 - 1,100,000; Full time

Job summary

Normal Computing, with teams across New York, Silicon Valley, London, Copenhagen, and Seoul, seeks a Role focused on building RL environments and data pipelines for chip-EDA related capabilities. You will own data strategy and collaborate with external vendors to improve model performance in real-world settings.

The position combines applied research with hands-on data work, emphasizing reinforcement learning, reward design, and rigorous evaluation across domains in AI-driven hardware

Qualifications

• Experience with post-training large language models for specific domains or real-world use cases.
• Experience with reinforcement learning, reward design, or training data curation for LLMs.
• Ability to manage technical vendor relationships and iterate quickly on feedback.
• Interest in reading datasets to understand them and spot issues.

Responsibilities

• 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.

Skills

Reinforcement Learning

Data Curation

Vendor Management

Cross-Functional Collaboration

LLM Domain Knowledge

RL Environments

Domain Expertise in Chip/EDA

Job description

About Normal Computing

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.

We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.

Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.

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

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 [e-mail].

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