AI Engineering Manager Jobs:
Lead the Teams Building the Future

It depends on what energizes you. AI Engineering Managers spend 60-70% of their time on people (1:1s, hiring, performance), 20-30% on strategy and alignment, and only 10-20% on technical work. If you love deep technical problems, consider Staff/Principal IC paths. If you want to multiply impact through others and shape team direction, management is the right move. Both paths can reach $300K+ at top companies.

AI Engineering Manager compensation typically ranges from $200K-$350K+ total comp, depending on company tier and location. At FAANG and well-funded AI companies, senior AI EMs (Director+) can exceed $400K-$500K with equity. Base salaries are typically $180K-$280K with bonuses and equity on top. The premium over traditional EM roles reflects the scarcity of leaders who combine deep AI understanding with proven management skills.

The IC-to-manager transition requires intentional skill-building before you get the title. Start by taking on tech lead responsibilities: mentoring juniors, leading projects, representing your team in cross-functional meetings. Volunteer to run hiring loops and onboarding. Document your impact in terms of team outcomes, not personal contributions. When you interview, demonstrate that you've already been doing the job unofficially. Many companies prefer promoting internally, so signal your interest early.

Yes, but you need to close the technical credibility gap. AI teams respect managers who understand what they're building, even if you're not coding daily. Invest time in understanding ML fundamentals, experiment tracking, model evaluation, and production ML challenges. Take an AI project from prototype to production, even as a side project. The management skills transfer directly; the AI-specific context is what you need to add.

AI EMs split time across several areas: (1) People management: 1:1s, career development, performance reviews, hiring (40-50%), (2) Project/program management: sprint planning, roadmap alignment, dependency management (20-30%), (3) Technical guidance: architecture reviews, technical decisions, unblocking the team (15-25%), (4) Stakeholder management: aligning with product, data science, and leadership (15-20%). Unlike IC work, success is measured by team output, not personal contributions.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.