How to Work with ML Engineers

The fastest way for a PM to create friction with ML engineers is to treat model work like normal feature work. Traditional software is mostly deterministic: define the behavior, implement the logic, test the edge cases, ship. ML work is probabilistic. The team is not just building a feature. They are managing data quality, model behavior, evaluation uncertainty, latency, cost, and drift over time.

That means the shape of collaboration is different. A good ML engineer does not want a PM to prescribe the model architecture. They want a clear problem statement, the decision the product must support, the user-visible failure modes that matter, and the business tradeoffs around quality, speed, cost, and safety. Your job is to make the product problem legible enough that engineering can choose the right technical path.

Think of the ML engineer as a partner in product discovery, not a late-stage implementation owner. They can tell you when a requirement implies unavailable labels, when a metric is unmeasurable, when a demo will not survive production traffic, or when a simple rules system beats a model. Bring them in early, and you avoid weeks of rework.