How to Get a Job in Edge AI: Essential Skills for 2025

Edge AI refers to artificial intelligence that runs directly on devices at the “edge” of the network: things like smartphones, smart cameras, industrial sensors, autonomous vehicles, and IoT devices. Instead of sending data to remote servers for processing, edge AI performs inference and decision-making locally on the device itself. This architecture delivers crucial advantages: reduced latency for real-time applications, enhanced privacy by keeping sensitive data on-device, lower bandwidth costs, and the ability to function without constant internet connectivity. From facial recognition on your iPhone to predictive maintenance sensors in factories, edge AI is transforming how intelligent systems operate in the real world.

In many ways, edge AI represents the natural evolution of the Internet of Things (IoT). Early IoT devices were primarily “dumb” sensors that collected data and transmitted it elsewhere for analysis. Edge AI adds the intelligence layer directly into these embedded systems, enabling them to understand context, make decisions, and act autonomously. A smart thermostat evolves from simply reporting temperature to predicting your preferences and optimizing energy usage. A network router moves beyond packet forwarding to detecting security threats in real-time. This convergence of IoT connectivity with on-device intelligence creates systems that are simultaneously smarter and more responsive.

As a result, the demand for edge AI skills has exploded beyond traditional AI development roles. Companies need professionals who understand not just machine learning algorithms, but also embedded systems, resource optimization, networking protocols, and the unique constraints of deploying AI where power, memory, and compute are limited. It’s a specialty that sits at the intersection of AI, embedded engineering, and systems design.

Math and Machine Learning Foundations

Before you can optimize a neural network to run on a microcontroller or deploy a computer vision model to a smartphone, you need to understand the mathematical principles that make machine learning work. Linear algebra forms the backbone of machine learning (ML). This includes topics like matrices and vectors. They control how neural networks represent data and transformations, and understanding operations like matrix multiplication, eigenvalues, and dimensionality reduction is essential for grasping how models process information.

Here are some courses I recommend for linear algebra:

Calculus, particularly derivatives and gradients, explains how models learn through backpropagation and gradient descent. Statistics and probability are equally critical: you need to understand distributions, sampling, hypothesis testing, and concepts like bias-variance tradeoff to evaluate model performance, understand uncertainty, and make informed decisions about when a model is reliable enough for deployment. While you don’t need a PhD in mathematics, a solid working knowledge of these areas (enough to read research papers and understand what’s happening under the hood) is fundamental.

Some calculus and probability/statistics courses I recommend:

Beyond pure math, you need core machine learning knowledge: understanding different model architectures (CNNs for vision, RNNs for sequences, transformers for language), knowing when to use classification versus regression, and grasping concepts like overfitting, regularization, and cross-validation. You should be familiar with the complete ML workflow: data preprocessing and feature engineering through model training, evaluation, and validation. Understanding how to split datasets properly, choose appropriate metrics for your problem, and diagnose issues like underfitting or overfitting will serve you throughout your career. This foundational knowledge applies regardless of where your models eventually run, and it’s the prerequisite for everything else you’ll learn in AI (including edge AI)

Here are a few of great ML courses I recommend:

Core Programming Languages

Edge AI demands versatility across multiple programming languages, each serving different roles in the development and deployment pipeline. Python is your primary language for machine learning development: it’s the lingua franca of AI, with rich ecosystems like TensorFlow, PyTorch, and scikit-learn. You’ll use Python for data preprocessing, model training, experimentation, and prototyping. Most ML research and tutorials assume Python knowledge, so fluency here is non-negotiable. Beyond just syntax, you should be comfortable with NumPy for numerical computing, Pandas for data manipulation, and Jupyter notebooks for interactive development.

For learning the basics of Python, I recommend:

For learning how to use Python for deep learning, I highly recommend Deep Learning with Python. I personally worked through the book, and it’s an amazing resource.

However, Python alone isn’t enough for edge deployment. C and C++ become critical when you need performance and efficiency on resource-constrained devices. Embedded systems, microcontrollers, and many edge inference engines are written in C/C++ because these languages offer fine-grained control over memory management and hardware resources. You’ll encounter C++ when optimizing inference engines, writing custom operators for neural networks, or deploying models on devices where every millisecond and milliwatt counts.

I recommend first starting off with learning the basics of C and C++. This highly rated set of courses on Coursera should be a good starting point. From there, you should learn how to work with embedded systems. Arduino is a good starting point, but you’ll eventually want to branch off to more professional frameworks. I recommend picking a chipset you enjoy (like STM32 or ESP32) and learning how to write code for their non-Arduino frameworks. Here are some courses to get you started:

For mobile platforms, you need platform-specific languages: Swift for iOS development with Core ML, and Kotlin or Java for Android with TensorFlow Lite and ML Kit. These languages let you integrate AI models into mobile apps, handle device sensors, manage user interfaces, and navigate platform-specific APIs. While you don’t need to be an expert in all of these languages simultaneously, understanding when and why each is used will make you far more effective in edge AI roles.

I honestly do not work much with mobile development, but I can offer some courses I have found. Code Academy has a number of courses on:

Edge AI Platforms and Hardware

Edge AI spans a remarkably diverse hardware landscape, and understanding the capabilities and constraints of different platforms is essential for successful deployment. Smartphones represent one of the most powerful edge AI platforms, with modern devices featuring dedicated neural processing units (NPUs) alongside CPUs and GPUs. iOS devices leverage Apple’s Neural Engine through the Core ML framework, while Android devices use a variety of chipsets with their own AI accelerators, accessible through TensorFlow Lite and Android’s Neural Networks API. These platforms offer relatively abundant compute resources, sophisticated sensors (cameras, microphones, accelerometers), and mature development ecosystems—but you still need to balance model performance against battery life and thermal constraints.

Beyond smartphones, edge AI extends into networking gear and edge gateways (e.g. routers, switches, industrial edge servers) that sit between IoT devices and the cloud. These systems might run on ARM processors with Linux, performing tasks like network traffic analysis, security threat detection, or aggregating and preprocessing data from connected sensors before sending insights to the cloud.

Then there’s the world of embedded systems and microcontrollers: devices like Arduino boards, ESP32 chips, Raspberry Pi single-board computers, and NVIDIA Jetson modules. This category ranges from tiny, power-sipping microcontrollers with just kilobytes of RAM running simple inference tasks, to Raspberry Pi-class devices with full Linux environments, all the way up to Jetson boards with dedicated GPU acceleration for computer vision.

Each platform presents different tradeoffs: microcontrollers offer ultra-low power consumption but minimal compute; Jetson boards provide serious AI horsepower but require active cooling and more power. Understanding these hardware constraints (available memory, processing speed, power budget, thermal limits, and available I/O) shapes every decision you make about model architecture, optimization, and deployment strategy.

Systems and Infrastructure Skills

You will likely need some basic operating system and infrastructure skills to implement edge AI projects. Linux is common in the edge AI world, powering everything from Raspberry Pi devices to industrial edge servers to network appliances. You need practical Linux administration skills: navigating the command line confidently, managing processes and services, understanding file systems and permissions, installing and configuring software packages, and troubleshooting system issues. Beyond basic administration, shell scripting (primarily bash) is invaluable for automating deployment tasks, preprocessing data, monitoring system health, and orchestrating ML inference pipelines. You might write scripts to collect sensor data, trigger model inference at specific intervals, manage log files, or automatically restart services when they fail.

The Linux Foundation has a great getting started course that covers these topics.

Embedded Linux environments present their own unique challenges and require specialized knowledge. Platforms like Raspberry Pi, NVIDIA Jetson, and various industrial computing modules run stripped-down Linux distributions optimized for specific hardware. You’ll need to understand cross-compilation (building software on your development machine for a different target architecture), working with device trees to configure hardware, managing limited storage space, and optimizing boot times.

For even more resource-constrained devices (e.g. microcontrollers running models with TensorFlow Lite Micro or similar frameworks) you may encounter Real-Time Operating Systems (RTOS) like FreeRTOS or Zephyr. While not every edge AI role requires deep RTOS expertise, understanding the basics of task scheduling, interrupt handling, and deterministic behavior helps you appreciate the constraints and capabilities of the smallest edge devices. These systems skills form the bridge between your ML models and the physical hardware they run on.

For FreeRTOS and Zephyr, I recommend the following courses:

Introduction to RTOS (covers FreeRTOS on the ESP32)
Introduction to Zephyr

Networking and Connectivity

In many ways, I consider edge AI to be an extension of IoT. Understanding networking fundamentals is essential for designing systems that are reliable, secure, and efficient. At the foundation, you need a solid grasp of TCP/IP networking: how devices get IP addresses, how routing works, the difference between TCP and UDP, and basic concepts like ports, firewalls, and NAT. This knowledge helps you troubleshoot connectivity issues, configure edge devices on local networks, and understand the security implications of networked AI systems.

Coursera has a good course on the fundamentals of TCP/IP.

WiFi and Bluetooth are the primary wireless technologies for edge AI devices. WiFi provides higher bandwidth for applications that need to transmit video streams, large datasets, or frequent model updates, while Bluetooth Low Energy (BLE) is perfect for power-constrained devices like wearables and sensors that send small amounts of data intermittently.

Beyond these consumer protocols, you should be familiar with IoT-specific communication protocols that are optimized for edge environments. MQTT (Message Queuing Telemetry Transport) is a lightweight pub-sub protocol widely used in IoT for efficient, asynchronous communication between devices and servers. CoAP (Constrained Application Protocol) is designed for extremely resource-limited devices with minimal overhead. Understanding these protocols helps you design efficient edge-to-cloud communication patterns (i.e. knowing when to send raw data versus processed insights, how to handle intermittent connectivity, how to batch messages to reduce power consumption, and how to implement offline-first architectures where edge devices continue functioning even when cloud connectivity is lost).

My IoT Firmware Development with ESP32 and ESP-IDF course offers a good intro to WiFi, SSL/TLS, and MQTT
Nordic’s DevAcademy has a great course on Bluetooth Low Energy (BLE)

Edge-Specific ML Tools and Frameworks

Deploying ML models to edge devices requires specialized tools and frameworks designed for resource-constrained environments. LiteRT (previously TensorFlow Lite) is Google’s solution for mobile and embedded deployment, allowing you to convert standard TensorFlow models into a compressed format optimized for edge inference. It supports hardware acceleration on mobile devices and provides variants like LiteRT for Microcontrollers (previously TensorFlow Lite Micro) work with extremely limited resources.

Core ML is Apple’s framework for iOS and macOS, offering tight integration with Apple’s Neural Engine and excellent performance on iPhones, iPads, and Macs.

ONNX Runtime (Open Neural Network Exchange) provides cross-platform model deployment, allowing you to train in PyTorch or TensorFlow and deploy to various edge platforms using a standardized format. Each framework has its strengths, and experienced edge AI practitioners often work with multiple tools depending on the target platform.

The real skill in edge AI lies in model optimization techniques that make sophisticated models viable on constrained hardware. Quantization reduces model size and speeds up inference by converting 32-bit floating-point weights to 8-bit integers (or even lower precision), often with minimal accuracy loss (for example, turning a 100MB model into a 25MB one). Pruning removes redundant or low-impact connections in neural networks, creating sparse models that require less computation. Knowledge distillation trains smaller “student” models to mimic larger “teacher” models, compressing knowledge into more efficient architectures.

Beyond software optimization, understanding hardware accelerators gives you significant performance advantages. Edge TPUs (Tensor Processing Units), Apple’s Neural Engine, Qualcomm’s AI Engine, and various NPUs provide specialized silicon for neural network operations, delivering orders of magnitude better performance-per-watt than general-purpose CPUs. Knowing how to profile your models, identify bottlenecks, choose appropriate optimization techniques, and leverage available hardware acceleration is what separates edge AI practitioners from general ML engineers.

For smartphone-based AI, here are a few courses you might want to consider:

As microcontroller-based AI (often called “TinyML”) is a niche area, there are only a few courses available that dive into running deep learning models on microcontrollers:

Soft Skills & Professional Development

Technical skills alone won’t make you successful in edge AI. The field demands strong collaboration and communication abilities. Edge AI projects inherently require cross-functional collaboration between data scientists, embedded engineers, hardware designers, mobile developers, and product managers. You need to communicate effectively across these disciplines, translating between ML terminology and hardware constraints, explaining model limitations to non-technical stakeholders, and understanding enough about each domain to have productive conversations.

Critical thinking about tradeoffs is essential: there’s rarely a single “correct” answer in edge AI, only different balances between accuracy, latency, power consumption, model size, and cost. You must evaluate these tradeoffs in the context of specific use cases and advocate for sound engineering decisions. Strong documentation and technical communication skills help you write clear deployment guides, explain model performance characteristics, and share knowledge with teammates.

Finally, edge AI is rapidly evolving. New hardware platforms, optimization techniques, and frameworks emerge constantly. Cultivating a continuous learning mindset, staying current with research, experimenting with new tools, and being comfortable with ambiguity will keep your skills relevant in this fast-moving field!

Conclusion: Building Your Edge AI Skillset Strategically

Breaking into edge AI requires a deliberate, layered approach to skill-building. Start with the fundamentals: solidify your understanding of mathematics, core machine learning concepts, and Python programming. From there, branch out based on your target platform: if you’re drawn to mobile development, dive into Swift or Kotlin alongside Core ML or TensorFlow Lite; if embedded systems excite you, focus on C/C++ and Linux administration. Don’t try to master everything at once, as edge AI is inherently interdisciplinary, and most professionals develop deep expertise in 2-3 areas while maintaining working knowledge of the rest.

Once you have a solid foundation, build projects that demonstrate your abilities (and excite you!): deploy a simple image classifier to a Raspberry Pi, create a mobile app with on-device inference, or optimize a model to run on a microcontroller. The field moves quickly, so cultivate curiosity and commit to continuous learning through online courses like the ones mentioned above, research papers, and hands-on experimentation.

Edge AI sits at the exciting intersection of machine learning, embedded systems, and real-world deployment. The demand for professionals who can navigate this convergence is only growing. With the right combination of technical skills, systems thinking, and practical experience, you’ll be well-positioned to build the intelligent, responsive devices that are shaping our future!

If you would like to dive into more embedded development, check out my selection of courses.