Learnings from Cultivating Machine Learning Engineers as a Team Manager

"As an AI team manager, Vivek Gupta stays broadly informed to guide AI experts effectively and drive the team. Engineers need feedback on both technical and interpersonal skills, Gupta mentioned in his talk Growing and Cultivating Strong Machine Learning Engineers at Dev Summit Boston. He stresses learning time, asking for help, and cross-team collaboration. Mentorship, data handling, and human-in-the-loop validation are key to success for machine learning engineers."

"One of the primary things engineers are seeking is feedback. They've just come out of school and are used to getting grades, and they want to know how they can do better, Gupta explained: Feedback is a very varied thing. Some of it will be about how they are doing in their coding areas, some of it is actually on how to interact with others, or how to deal with other people and teams that they're working with."

"Gupta mentioned that we need to get engineers to ask questions. Typically, they're not asking questions until they've really been stuck for much too long, he added. We need to actually encourage them to go to the senior engineers and managers, and ask if they know somebody who might be able to unblock them. We want people to talk to other disciplines, talk to people on other projects in order to foster collaboration, Gupta explained: Often, there are ideas on other teams that may allow them to leverage work that somebody else has done or share something that they've done to reduce duplicate effort. Encourage that type of collaboration or li"

AI managers should maintain broad knowledge to guide machine learning experts, understand applied sciences, and generate ideas that move teams forward. Engineers require feedback on both technical work and interpersonal interactions to improve performance. Teams should allocate time for learning, experimentation, and practice to build skills. Encourage engineers to ask questions early and seek help from senior engineers or managers to reduce prolonged blockers. Promote cross-team communication to leverage existing work and reduce duplication. Emphasize mentorship, careful data handling, and human-in-the-loop validation to increase the reliability and effectiveness of ML systems.

Read at InfoQ