Growing Engineers into Technical Leaders in AI Teams

One of the most rewarding parts of engineering management is watching someone grow from a strong individual contributor into a leader who elevates the entire team. But this transition doesn't happen automatically - it requires intentional coaching, the right opportunities, and patience.

I've been fortunate to guide several engineers through this transition on ML platform teams. Here's what I've learned about making it work.

The Misconception About "Natural Leaders"

There's a persistent myth that some engineers are "born leaders" and others aren't. In my experience, this is mostly wrong. What looks like natural leadership ability is usually the result of someone having had the right opportunities early in their career - code reviews where they learned to give constructive feedback, projects where they had to coordinate with other teams, debugging sessions where they learned to mentor.

The engineers who seem to lack leadership potential often just haven't had these experiences yet. The job of a manager is to create them deliberately.

Start with Ownership, Not Authority

The biggest mistake I see is promoting someone to "tech lead" and expecting them to figure it out. This puts them in an authority position before they've developed the skills to use that authority well.

Instead, I start by expanding ownership incrementally:

Phase 1: Own a component or service end-to-end
Phase 2: Lead technical decisions for a small project
Phase 3: Mentor one junior engineer
Phase 4: Drive cross-team technical alignment
Phase 5: Own team-level technical strategy

Each phase builds on the previous one. You can't effectively drive cross-team alignment if you haven't first learned to make sound technical decisions on your own.

The Art of the Stretch Assignment

The key to developing engineers is giving them challenges that are hard enough to grow them but not so hard they fail catastrophically. I call this the "stretch assignment" - something just beyond their current capability.

For someone ready to start leading, this might look like:

• Owning the technical design for a feature that touches multiple services
• Running a post-mortem after an incident (with coaching on how to facilitate)
• Representing the team in a cross-functional planning meeting
• Mentoring an intern or new hire through their first project

The critical part is the debrief after each assignment. What went well? What would they do differently? What support did they need that they didn't have?

Creating Safe Spaces to Fail

In ML and AI work, we deal with complex systems where cause and effect aren't always clear. A model might underperform for reasons that take weeks to diagnose. A data pipeline might have subtle bugs that only surface under specific conditions.

This complexity makes it essential to create psychological safety for emerging leaders. They need to know that:

1. Mistakes are expected and won't damage their career
2. Asking for help is a sign of strength, not weakness
3. Their manager has their back when things go wrong

I've found that the best way to create this safety is to share my own failures openly. When I talk about production incidents I caused or technical decisions I got wrong, it normalizes the learning process.

Technical Credibility Through AI/ML Challenges

One unique aspect of leading AI teams is that technical credibility matters enormously. Engineers won't follow someone who doesn't understand the domain. This means emerging leaders need opportunities to demonstrate technical depth, not just coordination skills.

Some ways I've seen this work well:

• Deep-dive investigations: Assign them to figure out why a model's performance degraded, then present findings to the team
• Architecture reviews: Have them lead the technical review for a new ML pipeline component
• Research synthesis: Task them with summarizing recent papers relevant to the team's work and recommending what to adopt

These assignments build both technical skills and the communication abilities needed to lead.

The Transition to People Leadership

Some technical leaders eventually want to move into people management. This is a different skill set entirely, and not everyone wants it - which is fine. Strong tech leads who don't manage people are incredibly valuable.

For those who do want to manage, I ease them in gradually:

1. Start with feedback: Have them lead performance conversations for one person, with coaching
2. Add hiring: Include them in interview loops, then have them lead a hiring decision
3. Own career development: Give them responsibility for one person's growth plan
4. Full management: Take on a small team with the support of a skip-level manager

The key insight is that management is about people, not process. The engineers who transition best are the ones who genuinely care about their teammates' success, not just their own technical achievements.

Measuring Growth

How do you know if your development efforts are working? I track a few signals:

• Increased autonomy: Are they making decisions independently that they used to escalate?
• Team impact: Are other team members citing them as helpful or influential?
• External recognition: Are engineers from other teams seeking their input?
• Quality of questions: Are they asking better questions in design reviews?

These are soft metrics, but they paint a picture over time.

The Patience Required

Developing leaders takes longer than you expect. I've seen the transition from strong IC to effective tech lead take 12-18 months of deliberate effort. Rushing it produces people with titles but not skills.

The investment is worth it. A team with multiple strong technical leaders is far more resilient than one that depends on a single person. And the engineers you develop will remember that you believed in them before they believed in themselves.