Engineering Program Manager – Machine Learning Features

Apple

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About The Position

The Engineering Program Manager will turn advanced machine learning in speech, audio, and computer vision into customer-facing features. The role works across ML research, data collection, silicon, firmware, device, and companion software teams, and owns the plan for moving ML features from research through release. This includes data planning, model training and evaluation, optimization for Apple silicon, software integration, validation, and release.

The program manager keeps teams aligned, surfaces risks early, and provides leadership with clear, data-driven recommendations when model quality, performance, and schedule involve trade-offs. The role also coordinates model milestones with hardware builds, pre-silicon validation, and silicon bring-up, and establishes practices for model readiness, versioning, regression tracking, and post-integration monitoring.

Requirements

• Bachelor of Science in Computer Science, Electrical Engineering, or a related STEM field.
• At least 8 years of relevant experience, including significant engineering program management experience in software or ML development.
• Hands-on background as an ML engineer, researcher, or data scientist.
• Experience working with data teams to define data strategy and plans for ML programs, with a clear understanding of how data affects model quality.
• Solid understanding of the ML lifecycle and sufficient technical depth to engage credibly with researchers and engineers.
• Experience with hardware NPI cycles, including prototype builds and pre-silicon validation.
• Use of AI tools in daily work to improve productivity.
• Excellent communication skills, including the ability to summarize complex status for executive audiences.
• Experience leading through influence in a matrixed, multinational environment.

Nice To Haves

• M.Sc. or Ph.D. in a related field.
• Experience with speech, audio, or computer vision ML, such as ASR, voice isolation, or video restoration.
• Familiarity with large-scale data collection and annotation practices, including privacy considerations.
• Experience with on-device ML and optimization for dedicated hardware or neural engines.
• Familiarity with MLOps practices, including data pipelines, distributed GPU training, and CI/CD for models.
• Experience with platform software release processes and working with US-based teams.
• Comfort with ambiguity and calm under pressure.

Responsibilities

• Own delivery plans for ML features from research and prototyping through integration, validation, and release.
• Partner with data teams to develop data strategies and plans that meet model needs and program milestones, including data type, scale, coverage, and timing.
• Align model milestones with hardware builds, pre-silicon validation using simulation and FPGA, and silicon bring-up.
• Lead integration with software groups, including model handoffs, interfaces, integration schedules, and release timing.
• Align ML teams, MLOps, and data science on priorities, compute capacity, and dependencies.
• Track model quality, latency, memory, and power against product targets, and lead trade-off decisions.
• Establish model release practices, including readiness gates, versioning, regression tracking, and post-integration monitoring.
• Summarize ML progress in clear, data-driven recommendations for leadership.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior