About the job
Join Gradient Labs to Revolutionize Customer Experience in Financial Services
At Gradient Labs, we are on a mission to create an innovative AI agent that transforms customer interactions and streamlines operational efficiency within the financial services sector.
Established in 2023, we have rapidly progressed from a concept to a dynamic team, serving renowned clients who appreciate our cutting-edge solutions. Our AI agent is distinctive, being the sole AI operations agent tailored specifically for financial services, adept at managing even the most complex and critical customer inquiries with utmost safety and efficiency. It acts as a singular agent for all customer operations, providing businesses with the extensive visibility and control necessary to ensure reliable outcomes.
Our team is small but formidable, composed of builders from prestigious organizations such as Monzo, Pleo, and Google. If you are passionate about solving challenging AI problems and wish to influence the future of customer operations and experiences, we eagerly await your application.
Your Role as an AI Engineer at Gradient Labs
As an AI Engineer, your primary responsibilities will include:
- Agent Enhancement: Develop, assess, optimize, and expand the capabilities of our AI agent. This entails preparing datasets, iterating on enhancements, and thoroughly testing and deploying successful methodologies into our production environment.
- Experimentation & Prototyping: Stay ahead of the curve by keeping up with the latest advancements in NLP and generative AI. You will explore and prototype innovative solutions to integrate state-of-the-art technologies into our platform.
- Data Analysis: Examine customer query datasets, support tickets, and related information to uncover insights and identify further automation opportunities for our agents.
Who We Seek
- Individuals eager to challenge the limits of autonomous agents that emulate human behavior.
- Self-driven professionals who thrive in a fast-paced setting rather than lengthy research cycles.
- Quick learners who can summarize, replicate, and test ideas from academic research.
- Impact-driven contributors focused on the practical applications of the products they develop rather than solely on research outcomes.
- Resilient problem-solvers who can navigate the uncertainties and setbacks that come with experimental work.

