Applied Research Engineer, Agents
As an Applied Research Engineer, Agents at Hebbia AI, you will bridge research and application, shaping core natural language processing systems. You will enable agentic capabilities across the product suite, conducting experiments with novel LLM techniques and building production-grade LLM-enabled software. This role requires expertise in NLP, machine learning systems, and LLM evaluation.
Hebbia is an AI platform for investors and bankers, generating alpha and driving upside. Founded in 2020 and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for major financial institutions including BlackRock, KKR, Carlyle, Centerview, and 40% of the world’s largest asset managers. Our flagship product, Matrix, provides industry-leading accuracy, speed, and transparency in AI-driven analysis, trusted to help manage over $30 trillion in assets globally. We deliver intelligence that gives finance professionals a definitive edge by uncovering signals, surfacing hidden opportunities, and accelerating decisions with unmatched speed and conviction. Hebbia transforms how capital is deployed, risk is managed, and value is created across markets.
The Agents team at Hebbia is responsible for building core document understanding capabilities, co-piloting experiences for Matrix, and deep, multi-source research functionalities. We have developed our own agentic frameworks powered by distributed systems built for scale, focusing on steerable, reliable, and explainable agentic systems that can handle vast amounts of customer data. Our goal is to create an indispensable and delightful product that unlocks unknown insights for customers worldwide, moving fast and building first-of-their-kind systems.
As an Applied Research Engineer, you will serve as the crucial link between research, industry, and application, significantly influencing the future of our core natural language processing systems. You will be instrumental in enabling agentic capabilities across the Hebbia product suite, owning experiments and Proof of Concepts (POCs) that combine the latest research findings with high-value problems faced by our customers daily. Leveraging our deep relationships with foundation model providers, you will partner to beta test models, experiment with new features, and develop guidance on relative model strengths.
This role demands prior expertise in NLP, machine learning systems, and LLM evaluation; experience building with foundation models and working with Attention-based NLP models is a strong plus. It is ideally suited for an individual who excels at both running experiments with novel LLM techniques and building production-grade, LLM-enabled software systems, embedding directly within the software development lifecycle.
Posted May 26, 2026