MacOS ML Engineer
Apple
On-site
Seattle, Washington, United States
Machine Learning Engineer
As a MacOS ML Engineer, you will build the fundamental software, libraries, tools, and test suites to support autonomous security on Apple devices.
You will develop the software and integrations to make on device security machine learning successful. As part of this process, you will define the architectures and services of autonomous security on-device, including OS interfaces and autonomous security capabilities. You will collaborate with Core OS, security, and services partners across Apple to deliver high reliability, high performance device frameworks and services.
You will adapt the intelligence, models, and research developed by the team to run on macOS. Development and deployment of autonomous security on macOS needs to balance privacy, rigor, visibility, performance, and impact. In this role, you need to have skills and knowledge across a blend of macOS development best-practices, systems and software engineering, and embedded systems development.
Day to day, you will use Apple internal tools and platforms, third party cloud, and local hardware to test and deploy software, frameworks, and ML models to target current macOS and future macOS releases.
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, or related field.
- 3+ years of experience developing system software, services, applications, or frameworks for macOS or Linux.
- Solid programming skills in at least one of the following programming languages: Swift, Objective-C, C/C++, Rust with strong attention to detail
- System level programming and debugging experience such as efficient file I/O, memory management, and concurrency.
- Experience with on-device ML frameworks (Core ML, Win ML, ONNX, TF Lite or ExecuTorch)
- Experience with API design and software architecture.
- Demonstrated experience working cross-organization
- Knowledge of general ML Framework implementation (Jax, PyTorch, or TensorFlow)
- Experience with continuous software monitoring and distributed systems.
- Experience with endpoint sensing, telemetry, or on-device stream processing.
- Understanding of MLOps practices including model validation, versioning, monitoring, and deployment in high-security environments.
- Background in cybersecurity, malware analysis, digital forensics, or red/blue teaming.
- Experience with CoreML, including porting models to run on macOS, utilizing the CoreML runtime, and deploying models using the Neural Engine or GPU.