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Software engineering manager

Cambridge
Global M
Software engineering manager
Posted: 3h ago
Offer description

Engineering Manager (Delivery & Execution Focus, AI-Native)

We are looking for an Engineering Manager who combines strong execution skills with a high sense of ownership. This is not a traditional people-manager role, it’s about driving outcomes, making pragmatic trade-offs, and ensuring delivery in a fast-paced environment.


What you’ll do

* Own engineering initiatives from concept to production and execute against a high-pressure roadmap
* Translate product direction into actionable plans and make clear decisions on priorities, scope, and trade-offs
* Ensure engineering quality, reliability, and pragmatic technical standards while balancing speed and technical debt
* Identify and remove blockers, streamline processes, and step in hands-on when needed
* Maintain high standards for team ownership, output, and accountability
* Work closely with Product as a delivery co-owner and contribute to senior-level strategy discussions


AI-Native approach

This role requires an Engineering Manager who is AI-native, not AI-curious. You should:

* Regularly use AI tools to increase personal and team leverage
* Apply AI to accelerate engineering execution and delivery speed
* Coach teams on responsible, effective AI usage
* Demonstrate sound judgment on where AI adds value and where it does not


The Environment

* Fully remote team
* Trade-offs and technical debt are part of day-to-day decisions
* Outcomes matter more than optics, and timely decisions often outweigh consensus
* Delivery pressure is real and sustained



A good fit if you:

* Have owned delivery for complex systems or products
* Have experience in fast-moving startup or scale-up environments
* Take accountability for outcomes and make decisions with incomplete information
* Remove blockers, accelerate delivery, and care about execution quality
* Use AI tools as a force multiplier for speed, quality, and impact

Not a fit if you:

* Prefer facilitation over decision-making
* Avoid performance conversations or hard trade-offs
* Rely heavily on process rather than execution
* View AI as optional or experimental

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