How Coding Agents Are Changing Machine Learning Engineering

Machine learning teams are moving faster, but the hard part has not disappeared. The work is shifting from writing and debugging every line of code toward defining the right problem, setting requirements, reviewing outputs, and deciding what belongs in a durable platform.Niels Bantilan, Chief Machine Learning Engineer at Union AI, explains how machine learning work has changed, why coding agents are accelerating prototyping, and what engineers must consider when building infrastructure that supports many teams instead of optimizing one model. He also shares how customer needs become product decisions, why machine learning roles are becoming more specialized, and why measuring AI productivity remains difficult.Key Takeaways• Coding agents reduce time spent on implementation, debugging, and exploration, but engineers still need judgment around architecture, quality, and business value.• Platform teams must balance experimentation with stability by giving users freedom at the edges while protecting a reliable foundation.• Machine learning engineering now spans a wider range of skills, from low level performance work to customer empathy, education, documentation, and developer advocacy.• The best model for a task may depend on complexity. Smaller self hosted models can handle tightly scoped changes, while longer and more complex work may still require stronger hosted tools.Episode Highlights00:50 What Union AI means by an AI runtime for production02:10 How machine learning work has changed over the past five years10:40 The mindset shift from model building to platform engineering15:00 Turning customer problems into reusable product capabilities19:00 Why machine learning roles are becoming more specialized21:50 Using coding agents through specifications, tickets, and code review26:50 Token costs, productivity measurement, and choosing the right modelOne Line That Stuck“I’m still solving problems. It’s just the level at which I’m doing it doesn’t require me to necessarily get into the weeds of the implementation.”Follow The Tech Trek for more conversations on AI, data, engineering, product, and technical leadership.