Apple · Seattle

AIML - Senior Embedded Machine Learning Engineer- Edge ML

(w/m/d) · 11.4.2025

Description

You will play a key role in designing, implementing, and optimizing ML solutions for highly constrained compute environments. This is a cross-disciplinary role that blends expertise in embedded systems, computer architecture, and machine learning to unlock new applications in areas such as IoT, wearables, robotics, and autonomous systems. RESPONSIBILITIES: - Design and implement embedded ML pipelines on microcontrollers and custom SoCs with tight compute, memory, and power constraints. - Optimize and quantize deep learning models for real-time inference on edge platforms. - Develop and maintain low-level firmware in C/C++ to integrate ML models with custom hardware accelerators and sensors. - Conduct performance benchmarking, memory profiling, and bottleneck analysis across various embedded platforms. - Collaborate closely with ML researchers, hardware architects, and product engineers to co-design efficient ML solutions from model training to deployment. - Evaluate new edge ML techniques, compilers (e.g., TVM, TFLite Micro, CMSIS-NN), and toolchains to advance the team's capabilities. - Contribute to the overall system architecture with a deep understanding of embedded compute, memory hierarchies, and data flow optimization.

Qualifications

  • Strong proficiency in C/C++ and Python, with a solid foundation in embedded firmware development.
  • Deep understanding of computer architecture, particularly ARM Cortex-M/A cores, SIMD, caches, memory alignment, and DMA usage.
  • Proficiency in model deployment tools and compilers such as TensorFlow Lite for Microcontrollers, TVM, ONNX Runtime, and custom model conversion pipelines.
  • Demonstrated expertise in performance analysis, using tools like perf, valgrind, gprof, or hardware-specific profilers.
  • Experience working with hardware interfaces such as SPI, I2C, UART, and integrating with sensors or custom accelerators.
  • Bachelor's, Master's, or PhD or equivalent experience in Computer Science or a related field.

Preferred Qualifications

  • Hands-on experience with deep learning concepts, including model architectures (CNNs, RNNs, Transformers), training workflows, and post-training optimization (quantization, pruning, distillation).
  • Familiarity with embedded RTOSes (e.g., FreeRTOS, Zephyr) and real-time application constraints.
  • Comfort with debugging low-level issues across software and hardware boundaries.
  • Excellent problem-solving and analytical skills with a thorough approach.
  • Most importantly: a strong curiosity, willingness to dive deep into unfamiliar problems, and an eagerness to learn and grow in a fast-evolving field.

Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $166,600 and $296,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Application

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