Embedded Tinyml information
See Boston, MA salary details
$86.3K - $96.6K
2% of jobs
$96.6K - $106.9K
3% of jobs
$106.9K - $117.1K
6% of jobs
$117.1K - $127.4K
5% of jobs
$127.4K - $137.7K
5% of jobs
$141.3K is the 25th percentile. Wages below this are outliers.
$137.7K - $147.9K
5% of jobs
$147.9K - $158.2K
7% of jobs
$158.2K - $168.5K
3% of jobs
$168.5K - $178.8K
3% of jobs
The median wage is $180.2K / yr.
$178.8K - $189K
58% of jobs
How much do embedded tinyml jobs pay per year?
As of Aug 27, 2026, the average yearly pay for embedded tinyml in Boston, MA is $166,636.00, according to ZipRecruiter salary data. Most workers in this role earn between $142,900.00 and $187,900.00 per year, depending on experience, location, and employer.
Embedded TinyML engineers specialize in developing and deploying machine learning models that run directly on small, resource-constrained embedded devices, such as microcontrollers and IoT sensors. Their work involves optimizing neural networks to ensure efficient performance with limited memory, power, and computational capacity. These engineers bridge the gap between embedded systems and AI, enabling smart features in everyday devices without the need for cloud connectivity.
To thrive as an Embedded TinyML Engineer, you need a solid background in embedded systems, machine learning fundamentals, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with microcontroller platforms (such as ARM Cortex-M), TinyML frameworks (like TensorFlow Lite for Microcontrollers), and hardware debugging tools is typically required. Strong problem-solving skills, attention to detail, and the ability to communicate technical concepts clearly set standout professionals apart in this field. These skills are crucial for efficiently developing, deploying, and maintaining machine learning models on resource-constrained devices, ensuring reliable and innovative edge AI solutions.
Deploying TinyML models on embedded devices often involves managing strict resource constraints, such as limited memory, processing power, and energy consumption. Developers must carefully optimize model size and inference speed while ensuring accuracy remains acceptable for the application. Additionally, integrating models within existing embedded system workflows and testing for reliability under various real-world conditions can be challenging. Collaborating with hardware engineers and firmware developers is crucial to achieve efficient deployment and seamless operation.
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