Engineering Manager - Artificial Intelligence

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