Tiny Ml information
See salary details
$59.5K - $72.5K
14% of jobs
$80.3K is the 25th percentile. Wages below this are outliers.
$72.5K - $85.6K
19% of jobs
$85.6K - $98.6K
12% of jobs
The median wage is $103.1K / yr.
$98.6K - $111.7K
17% of jobs
$111.7K - $124.7K
12% of jobs
$127K is the 75th percentile. Wages above this are outliers.
$124.7K - $137.8K
14% of jobs
$137.8K - $150.8K
7% of jobs
$150.8K - $163.9K
3% of jobs
$163.9K - $176.9K
0% of jobs
$176.9K - $190K
1% of jobs
How much do tiny ml jobs pay per year?
As of Sep 10, 2026, the average yearly pay for tiny ml in the United States is $111,632.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $132,500.00 per year, depending on experience, location, and employer.
Tiny ML, or Tiny Machine Learning, refers to the deployment of machine learning algorithms on tiny, low-power hardware devices such as microcontrollers. These devices typically have limited memory and processing power, making it challenging to run traditional ML models. Tiny ML allows for real-time data processing and decision-making directly on the device, reducing the need for cloud connectivity. It is used in applications like smart sensors, wearables, and IoT devices where efficiency and low power consumption are essential.
To succeed as a 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 relevant degree in computer science or electrical engineering. Familiarity with microcontroller platforms (such as Arduino or ARM Cortex), TensorFlow Lite, and model optimization tools is essential. Strong problem-solving skills, creativity, and effective communication help you adapt solutions to resource-constrained environments and collaborate across teams. These skills and qualities are crucial for delivering efficient, real-time AI solutions on edge devices where memory and power are limited.
Tiny ML engineers often encounter challenges related to limited computational resources, strict power consumption requirements, and minimal memory availability on edge devices. Optimizing machine learning models to maintain accuracy while significantly reducing their size and complexity is a key task. Additionally, ensuring compatibility with various hardware platforms and managing real-time data processing can be demanding. Collaboration with hardware engineers and embedded software developers is essential to address these constraints effectively.
Entry-level Tiny ML jobs typically involve developing and deploying machine learning models on embedded devices with limited resources. Candidates often need knowledge of embedded systems, programming skills in languages like Python or C++, and familiarity with TinyML frameworks such as TensorFlow Lite for Microcontrollers. These roles may include tasks like model optimization, sensor data processing, and working in collaborative development environments.
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