AIML - Sr Embedded Machine Learning Engineer, Edge ML & Ultra-Constrained Compute

Apple; Seattle (WA); On-site; USD 197,000 - 297,000; Full time

Job summary

A forward-thinking company is seeking a Senior Embedded Machine Learning Engineer to drive innovation in machine learning on edge devices. This role involves designing and optimizing ML solutions for constrained environments, collaborating with cross-disciplinary teams, and advancing cutting-edge techniques in embedded systems. You will be integral to developing solutions that bridge the gap between sophisticated ML models and low-power hardware, making significant contributions to areas like IoT and robotics. If you are passionate about pushing the boundaries of technology and eager to tackle complex challenges, this opportunity is perfect for you.

Benefits

Comprehensive medical and dental coverage

Retirement benefits

Employee stock purchase plan

Tuition reimbursement

Discretionary bonuses

Qualifications

• Strong proficiency in C/C++ and Python with embedded firmware development.
• Deep understanding of computer architecture and performance analysis.

Responsibilities

• Design and implement embedded ML pipelines on microcontrollers.
• Optimize deep learning models for real-time inference on edge platforms.

Skills

C/C++ programming

Python programming

Embedded firmware development

Performance analysis

Problem-solving skills

Education

Bachelor's degree in Computer Science or related field

Tools

TensorFlow Lite

TVM

ONNX Runtime

FreeRTOS

Zephyr

Job description

AIML - Sr Embedded Machine Learning Engineer, Edge ML & Ultra-Constrained Compute

Seattle, Washington, United States Machine Learning and AI

Summary

Posted: Apr 07, 2025

Weekly Hours: 40

Role Number: 200578228

We are looking for a highly motivated and experienced Senior Embedded ML Engineer to join our team focused on enabling cutting-edge machine learning capabilities on resource-constrained edge devices. In this role, you will be at the forefront of innovation as we bridge the gap between state-of-the-art ML models and the realities of low-power, real-time, embedded hardware.

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.

1. Design and implement embedded ML pipelines on microcontrollers and custom SoCs with tight compute, memory, and power constraints.
2. Optimize and quantize deep learning models for real-time inference on edge platforms.
3. Develop and maintain low-level firmware in C/C++ to integrate ML models with custom hardware accelerators and sensors.
4. Conduct performance benchmarking, memory profiling, and bottleneck analysis across various embedded platforms.
5. Collaborate closely with ML researchers, hardware architects, and product engineers to co-design efficient ML solutions from model training to deployment.
6. Evaluate new edge ML techniques, compilers (e.g., TVM, TFLite Micro, CMSIS-NN), and toolchains to advance the team's capabilities.
7. Contribute to the overall system architecture with a deep understanding of embedded compute, memory hierarchies, and data flow optimization.

Minimum Qualifications

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

Preferred Qualifications

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

Pay & 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 $197,400 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.

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

Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.