Remote Lead Engineer, AI Quality Platform

CIRCLE; Greater London; Remote; GBP 114,000 - 144,000; Full time

Benefits offered by this job

Remote work

Equity

35 days PTO

Job summary

Circle is building the world’s leading AI-powered platform for digital businesses. We are a fully remote company with ~270 teammates, seeking a Lead Engineer for the AI Quality engineering team.

You’ll design evaluation frameworks, observability tooling, and datasets, while leading a growing team and collaborating with AI Core engineering. You will own the evaluation infrastructure, diagnose quality issues, and drive cost and latency improvements through experiments and model swaps.

Qualifications

• 7+ years of experience building and shipping production software with LLN-powered agents.
• Comfortable in Ruby on Rails / Python or quickly picking them up.
• Experience building evaluation or observability infrastructure for ML/AI systems.
• Experience designing datasets, annotation workflows, or labeling pipelines for ML/AI evaluation.
• Learn fast, experiment aggressively, thriving in a dynamic environment.
• Comfortable in a fast-paced environment with ambiguity.
• Proficient in English at CEFR Level C2 / ILR Level 5.

Responsibilities

• Build and own evaluation infrastructure, including CI/CD pipelines, scorers, and datasets.
• Diagnose where quality breaks down across the agent pipeline and prototype fixes.
• Grow annotation workflows and AI-generated/simulated conversations.
• Run structured experiments across prompts, models, and agent harnesses.
• Lead and grow the AI Quality engineering team while staying hands-on.
• Partner closely with AI Core engineering to improve quality.

Skills

7+ years exp

Ruby on Rails

Python

Observability infra

Eval pipelines

Dataset design

English proficiency

Tools

Braintrust

LangSmith

CI/CD

Job description

Circle is building the world’s leading AI-powered platform for digital businesses. We are a fully remote company with ~270 teammates, seeking a Lead Engineer for the AI Quality engineering team.

You’ll design evaluation frameworks, observability tooling, and datasets, while leading a growing team and collaborating with AI Core engineering. You will own the evaluation infrastructure, diagnose quality issues, and drive cost and latency improvements through experiments and model swaps.