AI Engineering Manager

You have always wanted to lead. You showed signs early. You were highly analytical, so you became a consultant or an engineer in a field that rewarded that ability. You performed brilliantly, learned how to communicate with senior leaders, and were given a team or a pod to run.

That may have brought your first real setback. The speed and intensity that made you exceptional as an individual did not automatically help your teammates work at the same pace. If that sounds familiar, you learned from it. If your path was different, what matters is that you know how to bring a team with you.

You took the time and deep dove in AI. Not just basic Q&A like 99% of users, but top 1% of power-users with an army of self-improving agents that automated your life so you felt max productivity. Your best team mates, better than the best humans you’ve worked with. You grew stronger at both engineering and leadership.

What happens at 100x

In your first week, you will learn why the client project exists, meet the engineers, inspect the codebase and understand how each person works. You will turn that context into a concrete visual plan, share it with the client and the team, and take the reins.

Then you will run a daily delivery rhythm. You will get into the team’s workflows and bottlenecks, investigate failures several levels deep, test solutions and keep shipping. Each week, you will make progress, risks and decisions clear to the client. 

You will push for ambitious work while helping the team feel good about doing it. You will celebrate demanding sprints, make room to recover and coach people who can lead the next engagement. As 100x grows, you can move from leading one pod to developing leaders across several.

You will not have a cold-prospecting quota. You will sell through delivery: clients should want to keep building with 100x because of what your team ships and how you lead the work.

Essential must-haves

• Led engineers and shipped software or AI systems into production; able to make credible technical decisions and inspect the work yourself.
• Already use AI tools or agents to increase your own output, while checking their work critically.
• Gather context quickly across business goals, workflows, data and people; ask strong questions when the picture is incomplete.
• Communicate clearly with US clients and engineers, including through concise visual plans and honest risk updates.
• Raise a team’s pace through coaching and ownership, while learning from feedback and sustaining the people doing the work.