Senior Product Engineer (Agent Training and Evals)

Se nior Product Engineer (Agent Training and Evals)

Engineering

Prolific

Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.

The role

As a Senior Product Engineer, you'll be one of the early engineers in our Agentic Training & Evals team.

You'll build the infrastructure that lets us evaluate AI agents, alongside real human expert work in safe and secure environments.

This is a genuine 0→1 role. There isn't a well-established playbook or existing architecture for you to simply maintain. You'll have significant influence and ownership over the technical direction of the team and the products we build.

You'll work closely with engineering, product and AI research to turn ambiguous problems into scalable systems, and you'll see the impact of what you build directly.

What you’ll bring to the role

Strong experience building scalable infrastructure or distributed systems in the cloud

Solid understanding of how systems are deployed, scaled and secured

Hands-on experience with evaluating AI models or agents

Interest in how AI agents are trained, evaluated and improved

Bonus: experience with image, video, or audio processing

Strong product mindset with a focus on delivering business and customer value

Comfort with ambiguity, and the ability to move quickly from idea to working system

Strong communication and collaboration skills

Python experience is useful, but not essential

What you’ll be doing in the role

Solve hard problems around compute, orchestration, scaling, security and reliability

Help develop approaches for training, benchmarking and evaluating AI agents

Work with emerging agentic AI technologies and rapidly evolving models

Make pragmatic technical decisions in a highly ambiguous environment