Machine Learning Systems Engineer – Video Computer Vision
ML systems engineer on Apple's Video Computer Vision team, working across the stack to turn research into shipped on-device computer vision features: building software infrastructure, optimizing models for efficient embedded deployment, and contributing to post-training alignment/evaluation. Core stack: Python, C++, PyTorch.
- Compensation
- Not disclosed
- Location
- Sunnyvale
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Machine Learning, or related discipline plus 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 on embedded platforms
- Solid understanding of operating system fundamentals and extensive programming experience in Python and C++
- Hands-on PyTorch experience and familiarity with the end-to-end ML lifecycle (data preprocessing, training, evaluation, edge deployment)
- Experience with Supervised Fine-Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized on-device downstream tasks
- Robust understanding of ML architectures, specifically Multimodal LLMs, and integration of ML components into complex production systems
Nice to have
- 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)
Full description
The incredible potential of multimodal foundation models and large language models has unlocked machine learning applications that were previously thought infeasible. The Video Computer Vision (VCV) group is looking for a highly motivated and skilled Machine Learning Systems Engineer to help us ship cutting-edge computer vision technology on Apple devices.
The VCV organization has pioneered groundbreaking features like FaceID/FaceKit, Gaze/Hand Gesture Control, Body Tracking, and 2D/3D Scene Understanding fundamentally changing how millions of users interact with technology. We seamlessly balance research and product requirements to deliver pioneering, Apple-quality experiences. By innovating across the full stack and partnering closely with hardware, software, and AI teams, we shape future products and bring our architectural vision to life.
As a member of the Video Computer Vision team, you will work across the stack to transform promising research projects into refined products that ship to our customers. You will build software infrastructure to deliver multimodal context to models and optimize the software harness and the model for efficient on-device deployment. Partnering closely with algorithm researchers, you will also participate in post-training model alignment and evaluation.
Minimum Qualifications
- 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.
Preferred Qualifications
- 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).