Machine Learning Systems Engineer – Video Computer Vision - Jobs - Careers at Apple
- Develop on-device software that bridges multimodal AI models and computer vision technologies with production systems deployed across Apple devices.
- Optimize on-device inference latency, memory footprint, and computational efficiency of CV/ML models.
- Benchmark, profile, and evaluate the power consumption and thermal performance of models running on Apple silicon.
- Engage with cross-functional teams to understand and influence requirements, brainstorm the solution space, and propose, design and implement debuggable and maintainable Computer Vision systems meeting requirements.
- Design experiments and evaluations to understand quality and on-device deployment tradeoffs, driving informed system design decisions.
- Debug concurrent systems on embedded devices via a variety of existing tools and techniques; develop innovative debug tools and visualizations when existing ones don't fit the bill.
- Bachelor’s degree in Computer Science, Machine Learning, or a related discipline, and 3+ years of relevant industry experience.
- Strong ML fundamentals.
- Proven track record of writing high-quality production code for shipped on-device CV/ML features deployed on embedded platforms
- Solid understanding of operating system fundamentals and extensive programming experience in Python and C++.
- Hands-on experience with PyTorch and familiarity with the end-to-end ML lifecycle (data preprocessing, training, evaluation, and edge deployment).
- Experience with Supervised Fine-Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized, on-device downstream tasks.
- Robust foundational understanding of machine learning architectures, specifically Multimodal LLMs and the integration of ML components into complex production systems.
- Programming experience with Swift and familiarity with CoreML, CoreFoundation, and RealityKit frameworks.
- Fundamental knowledge of real-time video pipelines, image transformations, and rendering loops.
- Experience optimizing models for neural network accelerators (e.g., Apple Neural Engine or mobile GPUs).
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🇬🇧 English