Engineering Manager — Scale AI-driven enterprise

Sarah Smith Fund; Greater London; Hybrid; GBP 130,000 - 170,000; Full time

Benefits offered by this job

Equity

London office

Pension plan

2 weeks work-from-anywhere

Job summary

Kernel is building the business identity layer for agents, already trusted by major platforms. We seek a player-coach engineering leader to own delivery end-to-end, balance hands-on coding (~20%) with team leadership (80%), and set the technical direction for a rapid-growth startup in London.

You will hire, mentor, and align engineers with product goals, tackle hard engineering challenges, and drive architecture decisions that scale with customer complexity and data emphasis.

Qualifications

• 8+ years software engineering experience.
• Proven track record building and scaling distributed systems.
• Experience leading high-performing engineering teams in fast-paced environments.
• Ability to design enterprise-grade architectures and set technical direction.
• Experience collaborating with data engineers and ML teams is a plus.

Responsibilities

• Build, hire, and develop a high-performing engineering team.
• Help set technical direction and architecture decisions.
• Work with the engineering team to tackle difficult problems.
• Own engineering delivery with emphasis on velocity and quality.
• Collaborate with Product on roadmap and prioritisation.
• Write code when needed to stay close to the product.

Skills

Software engineering leadership

Distributed systems

Autonomy in startups

Architect enterprise-scale systems

Data-driven decision making

Tools

AWS

NodeJS

TypeScript

Postgres

Kubernetes

ClickHouse

NextJS

Tailwind

Job description

Kernel is building the business identity layer for agents, already trusted by major platforms. We seek a player-coach engineering leader to own delivery end-to-end, balance hands-on coding (~20%) with team leadership (80%), and set the technical direction for a rapid-growth startup in London.

You will hire, mentor, and align engineers with product goals, tackle hard engineering challenges, and drive architecture decisions that scale with customer complexity and data emphasis.