Engineering Program Manager – Machine Learning Features

Play a part in shaping the future of human-computer interaction. We're looking for an Engineering Program Manager (EPM) to turn advanced machine learning in speech, audio and computer vision into features that ship to customers. You'll work across ML research, data collection, silicon, FW, and the device and companion SW teams that bring these features to Apple customers.Your work will cover every stage, from the first dataset to the final release. You'll own the plan for taking ML features from research to release. That covers data planning, model training and evaluation, optimization for Apple silicon, integration with SW teams, and release. You'll keep teams aligned, make risk visible early, and help leadership decide when model quality, performance and schedule pull in different directions. You'll be comfortable with how uncertain research is, and disciplined about delivering what ships. Minimum qualifications: B.Sc. in Computer Science, Electrical Engineering, or a related STEM field 8+ years of relevant experience, including significant engineering program management experience in SW or ML development Hands-on background as an ML engineer, researcher or data scientist SW Experience working with data teams to define data strategy and plans for ML programs, with a clear understanding of how data drives model quality Solid understanding of the ML lifecycle, with the technical depth to engage credibly with researchers and engineers Experience with HW NPI cycles (prototype builds, pre-silicon validation) Using AI tools in daily work to improve productivity Excellent communication skills, including summarizing complex status for executive audiences Experience leading through influence in a matrixed, multinational environment Preferred qualifications: M.Sc. or Ph.D. in a related field Experience with speech, audio or computer vision ML (e.g., ASR, voice isolation, video restoration) Familiarity with large-scale data collection and annotation practices, including privacy considerations Experience with on-device ML and optimization for dedicated HW or neural engines Familiarity with MLOps practices: data pipelines, distributed GPU training, CI/CD for models Experience with platform SW release processes and working with US-based teams Comfortable with ambiguity, using AI tools in daily work, and calm under pressure