DimRed is building a platform to automate the most challenging parts of scaling AI Agents and LLM-based systems. We believe the path forward for AI systems is grounded in reliability and interpretability. Our platform leverages first principles-based thinking and ML theory to automate the manual process of iterating on prompts, LLM evaluations, and agent experimentation.
Our mission is to enable anyone to easily automate knowledge work with LLMs through task demonstration.
We are looking for a Founding AI Research Scientist who will be critical to the design and development of highly scalable, data-intensive AI systems. You’ll partner closely with the CEO and research scientists to bridge the gap between research and production, and your work will directly shape the foundation of DimRed's platform.
Design and implement LLM experiments to drive frontier research on how to overcome performance and interpretability challenges in LLM reasoning and Agent decision making.
Bridge theory and LLM experimentation into accessible AI products.
Build Agents and Workflows that automate tasks historically done by AI Engineers and Data Scientists.
Optimize complex LLM systems to ensure product viability and generalizability.
Work with customers to understand what limitations they are hitting with their LLM-based applications.
Advanced degree in Statistics, Economics, Math, Computer Science or related fields.
2–4 years of experience working on machine learning models and generating data insights in a professional capacity (startup experience preferred).
Familiarity with software engineering best practices.
Ability to thrive in an early-stage startup environment: ambiguity, rapid iteration, and direct customer feedback loops.
Competitive salary and top-of-market equity compensation.
Flexible PTO policy.
Professional development budget.
AI tooling budget.
Opportunity to shape the direction of a fast-growing startup.
As one of the first research scientists, you’ll shape our research direction, culture, and best practices. You’ll work at the intersection of AI research and production systems—turning ideas into infrastructure that scales. If you want to help define how reliable and interpretable AI agents are built, we’d love to talk.
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