
Hiring Engineering Manager (Americas)
AshbyWe’re looking to scale our engineering leadership team with technical leaders who want to raise the impact of a high-performing organization.
* Not Your Traditional "Jira-Manager": Our EMs are not classic delivery coordinators who shuffle meetings or build heavy processes. Instead, you will own people, hiring bar, technical judgment, and the health of our engineering systems.
* High-Autonomy Team: Because 80%+ of our engineering team is Senior–Staff+ level, our engineers already possess a high degree of ownership. Your focus is less on day-to-day task coordination and more on coaching independent owners, managing ambiguity, and raising team impact.
* Technically Credible Leaders: You will stay close enough to the work to challenge assumptions, review proposals, spot maintainability issues, and jump into the code to debug alongside engineers when needed.
* Organizational Advocates: Beyond your immediate team, you will act as an advocate owning the health and metrics of org-wide areas—ranging from CI/CD health and developer productivity to customer success collaboration and on-call sustainability.
* 100% Remote Async Culture: We maintain an even distribution of engineers across North America and Europe, optimizing for minimal process and maximum focus. * Here's how we're thinking about AI at Ashby:https://www.ashbyhq.com/blog/engineering/ai-ashby-engineerin...
* Full transparency on engineering levels + comp:https://www.ashbyhq.com/blog/engineering/leveling-and-compen...
Stack: TypeScript (frontend & backend), React, GraphQL API, Node.js, Postgres, Redis. (While you won't always be shipping code, you are expected to hold a deep technical understanding of the system).
Full list of open roles:https://www.ashbyhq.com/careers?utm_source=HNwhoshiring
Opens the company's application page
Listed via
Findwork
findwork.dev
Similar roles
Design & Tech
Related reads from TCHNX

The Inference Economy: Why AI’s Biggest Cost Shift Is Happening After Training
A major shift in AI economics is reshaping the industry. As training frontier models becomes more expensive and inference becomes dramatically cheaper, companies are being forced to rethink how they build, deploy, price, and monetise intelligent systems.

The Emergence of Small Language Models: Why Efficiency Is Overtaking Scale
As the AI industry confronts computational costs and environmental concerns, a new generation of compact models is proving that bigger isn't always better. Small language models are reshaping enterprise AI deployment.

Algorithmic Bias in Design Systems: Why Your AI-Generated UI Might Exclude Users
As AI tools increasingly generate interface components, they're embedding biases that systematically exclude users. Understanding how machine learning models inherit prejudice is essential for creating truly inclusive design systems.

