Описание

Own the delivery plan for ML features, from research and prototyping through integration, validation and release
Partner with data teams to build the data strategy and plan, making sure it covers what the models need and supports program milestones: what data, at what scale and coverage, and by when
Align model milestones with HW builds, pre-silicon validation (simulation and FPGA), and silicon bring-up, ensuring models are ready at each gate
Lead integration with SW groups, including model handoff, 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
Set up model release practices: readiness gates, versioning, regression tracking and post-integration monitoring
Summarize ML progress into clear, data-driven recommendations for leadership.

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

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

Engineering Program Manager – Machine Learning Features