The Shift from Feature Teams to Capability Teams
Traditional software teams organize around features: a payments team, a search team, a user-management team. But AI-native products demand a different structure. When your core differentiator is intelligence — not CRUD — the team topology must reflect that.
1. Embed ML Engineers in Product Squads
Don't create a separate "AI team" that throws models over the wall. Embed ML engineers directly in product squads. They sit in the same standups, own the same OKRs, and feel the same product pain.
2. Data Scientists as Product Partners
Data scientists shouldn't be a shared service. Pair each product squad with a dedicated data scientist who understands the domain, the metrics, and the user behavior. They become the squad's "intelligence PM."
3. Platform Team for MLOps
While ML engineers embed in squads, a dedicated platform team builds the shared infrastructure: feature stores, model registries, training pipelines, evaluation frameworks. This prevents every squad from reinventing the same plumbing.
Hiring for AI-Native Teams
We look for engineers who can bridge the deterministic and probabilistic worlds. A backend engineer who understands evaluation metrics. An ML engineer who cares about latency budgets and API contracts.
Measuring What Matters
Velocity and story points don't capture AI work. We track: model quality trends, data drift alerts, evaluation suite coverage, time-to-production for model updates. These are the metrics that predict product success.