Engineering Manager - Artificial Intelligence
20 hours ago
Sheffield
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