Quant Developer in AI

present of AI; Greater London; Híbrido; GBP 90.000 - 130.000; Jornada completa

Descripción de la vacante

Encord seeks a skilled engineer to build and run large-scale data systems from scratch, grounded in strong CS fundamentals and fluent in Python or TypeScript plus one of Rust, C++, Java or Go.

You will own production services, reason about failure modes and load, and learn rapidly in demanding settings, gaining direct exposure to data pipelines behind robotics and physical AI.

While prior work at a trading firm can be helpful, it is not required; curiosity and speed to learn are valued above all.

Formación

• Built and run large-scale data systems from scratch with core CS knowledge.
• Fluency in Python or TypeScript plus one of Rust, C++, Java or Go.
• Hands-on experience owning production services and reasoning about failure modes and load.
• Fast learner in demanding settings; adaptable and curious.

Responsabilidades

• Own and operate production data services and pipelines for AI-related workloads.
• Design and optimize distributed data systems, including video, sensor data and ML inference workloads.
• Reason about failure modes, load, and resiliency; implement robust monitoring and rollback plans.
• Learn quickly on the job to keep up with evolving AI data challenges.

Conocimientos

Python

TypeScript

Rust

C++

Java

Go

Distributed systems

Production service ownership

Fast learning under pressure

Descripción del empleo

The role asks for someone who has built and run large scale data systems from scratch, with strong core computer science knowledge and fluency in Python or TypeScript plus one of Rust, C++, Java or Go. A strong candidate brings hands on experience owning production services, reasoning about failure modes and load, and a record of fast learning in demanding settings. Prior work at a trading firm or exchange is helpful but not required.

How it fits the AI era

AI changes the work by making the data itself the hard problem: huge 3D scenes, long videos and sensor streams must be processed correctly and quickly to train physical AI systems. The human part stays in designing reliable distributed systems, judging tradeoffs and owning what ships. Curiosity and fast learning matter more than prior ML experience.

How it builds AI experience

The role offers direct exposure to the data pipelines behind robotics and physical AI, including large scale video, LIDAR and ML inference workloads. Someone can build relevant experience by working on distributed data systems, performance tuning and production ownership. Prior AI or ML knowledge is not required, so learning happens on the job.

Worth showing

• Python or TypeScript fluency
• Production service ownership
• Fast learning under pressure

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Encord

A data platform that helps AI teams manage, label and clean the video, sensor and image data that trains robots, self-driving cars and other physical AI systems.