7 Best-Paying Tech Skills Companies Are Hiring for Now

Learn about the 7 best-paying tech jobs, their main use cases and tech skills needed, and which companies are hiring.

AI expertise is in high demand, reflected in the top-paying tech roles today. Despite tech layoffs making news, many companies are pivoting to AI initiatives, leading to increased demand for related skills. A Deloitte survey of 100 corporate executives found that 45% of senior executives are actively training their teams in AI.

Most of the highest-paid tech skills revolve around AI, indicating that professionals with AI skills can excel in the job market and command higher salaries. Roles like generative AI engineer, ML engineer, and LLM developer top the list, emphasizing the importance of AI, deep learning, and computer vision expertise.

Curious about what tech skills companies need today that will make you over $150k a year? We've researched and compiled the 7 best-paying tech jobs for you below.

Generative AI job postings in the US and Europe

1. Generative AI Engineer

Salary Potential for Generative AI Engineers according to ZipRecruiter: $151,500 – 191,500

Generative AI Engineer tops the list of highest-paid tech roles, with an average salary potential of $191,500, according to ZipRecruiter. Major tech giants like Apple, Google, Meta, Microsoft, and OpenAI are collectively hiring 501 generative AI roles. Indeed's January 2024 Jobs Day report revealed that job listings mentioning generative AI as a skill offer salaries 47% higher than average. Searches for generative AI jobs on Indeed have surged by nearly 4,000% in the past year, with job postings seeing a remarkable 306% increase since September 2022.

Generative AI engineers specialize in creating AI systems capable of autonomously generating content like text, images, and music. Using deep learning and machine learning techniques, they train AI models on extensive datasets to produce new, original content that exhibits human-like creativity. Their expertise lies in algorithm optimization, data modeling, and applying AI to solve intricate, creative challenges.

Top Use Cases for Generative AI

Data Augmentation: Generative AI improves data quality by adding new information similar to the original dataset, improving its richness and diversity.
Designing Neural Networks: Generative AI assists in designing neural networks by exploring different configurations and optimizing connections between neurons, enhancing performance.
Multimodal AI for Creative Content: Combining text, images, and other data types, Generative AI produces creative content like blog posts, social media captions, and email newsletters, ideal for marketing campaigns.
SLMs: Small Language Models (SLMs) powered by Generative AI handle customer inquiries, generate responses, and automate routine support tasks, facilitating round-the-clock assistance through conversational chatbots.
Personalized Product Recommendations: Generative AI analyzes customer data to offer personalized product recommendations based on past purchases, browsing history, and demographics.
Natural Language Generation for Reports and Insights: In sectors like finance and healthcare, Generative AI processes vast datasets to generate detailed reports and insights in natural language.
Automated Code Generation: Generative AI automates software development by generating code snippets for various applications such as web development, APIs, and machine learning models.

Skills Needed for a Generative AI Engineer

Mastery of programming languages such as Python is crucial, along with expertise in AI-centric libraries like TensorFlow, PyTorch, and Keras.
Ability to implement and manipulate complex algorithms including generative adversarial networks (GANs) and variational autoencoders (VAEs).
Familiarity with techniques for text parsing, sentiment analysis, and the utilization of transformers like GPT.
Knowledge of data management principles, including data pre-processing, augmentation, and synthetic data generation.
Proficiency in cloud computing and deployment, particularly on platforms like AWS, Google Cloud, or Microsoft Azure.
Understanding of containerization technologies like Docker and orchestration tools like Kubernetes, crucial for scaling AI solutions.
Ability to assess model performance, interpret outcomes, and make data-driven decisions to improve model accuracy and efficiency.
Strong collaboration and communication skills for articulating technical details and project needs to both technical and non-technical stakeholders.

Hire high-performing Generative AI Developers from a vetted talent network →

2. Deep Learning Engineer

Salary Potential for Deep Learning Engineers according to ZipRecruiter: $151,500 – 191,500

Deep Learning is a type of machine learning inspired by how the human brain works. It's all about artificial neural networks that analyze data and make decisions like humans do. These networks use layers of algorithms to process information. This setup mimics the way our brains process data, leading to more advanced learning compared to traditional machine learning methods. Deep Learning can be used in both supervised and unsupervised learning scenarios, allowing computers to learn from data with or without human guidance.

A Deep Learning Engineer plays a crucial role in various phases of a project, from data engineering to modeling and deployment. They handle tasks like:

Data Engineering: Gathering, categorizing, analyzing, and cleaning project data to meet project requirements.
Modeling: Training deep learning models, developing evaluation metrics, and optimizing model hyperparameters.
Deployment: Converting prototype code into production-ready code and setting up cloud infrastructure to deploy the model for real-world use.

Top Use Cases for Deep Learning

Image Classification: Deep learning algorithms can be trained on large labeled image datasets to categorize new images based on their content. This technology is widely used for tasks like automatic image labeling on social media and object identification in autonomous vehicles.
Speech Recognition: Deep learning enables the conversion of spoken words into written text, facilitating applications such as speech-to-text transcription and real-time language translation.
Natural Language Processing (NLP): Deep learning models can understand and process human language, leading to applications like text summarization and language translation.
Image Recognition: Companies like Google, Facebook, and IBM utilize deep learning to train computers to recognize human faces and identify objects in images.
Translation: Once trained on one language, deep learning systems can be expanded to understand and translate multiple languages, facilitating cross-language communication.
Analytics: Deep learning improves predictive and prescriptive analytics, making big data analysis more effective for businesses across various industries.

Skills Needed for a Deep Learning Engineer

Understanding of deep learning models for computer vision tasks, including object recognition and segmentation.
Proficiency in Python programming and hands-on experience with at least one deep learning framework such as PyTorch, TensorFlow, Caffe2, or MXNet.
Familiarity with task management and containerization using Kubernetes.
Expertise in research and development pipelines for distributed large-scale training or automating complex MLOps pipelines with consistent release intervals.
Knowledge of building and deploying machine learning pipelines using cloud platforms like GCP, Azure ML, or AWS SageMaker.
Familiarity with DL frameworks for convenience, such as PyTorch Lightning or DL-Catalyst.
Ability to work effectively in a fast-paced development environment.
Strong teamwork and communication skills for articulating technical details and project needs to both technical and non-technical teammates and stakeholders.

Hire high-performing Deep Learning Engineers from a vetted talent network →

3. Computer Vision Engineer

Salary Potential for Computer Vision Engineers according to ZipRecruiter: $150,666 – $211,000

Computer Vision Engineers are skilled professionals who blend expertise in computer science, machine learning, and image processing. Their primary role is to create algorithms and systems enabling computers to comprehend and interpret visual data, much like human vision. Using cutting-edge techniques in deep learning and artificial intelligence, these engineers develop groundbreaking solutions that empower machines to identify patterns, make informed decisions, and engage with their surroundings based on visual cues.

Top Use Cases & Specializations for Computer Vision Engineer

Algorithm Development Engineer: These engineers design and implement mathematical models that enable computer vision systems to process visual data. They specialize in image recognition, object detection, and scene understanding, contributing significantly to industries like robotics and security.
Machine Learning Engineer for Computer Vision: Focused on creating and training models using deep learning techniques, these engineers teach machines tasks like image classification and facial recognition. Their expertise is vital in various applications, including gesture analysis and autonomous vehicles.
Embedded Computer Vision Engineer: Specializing in integrating computer vision capabilities into hardware devices, these engineers optimize algorithms for efficient performance on embedded systems like mobile phones and drones.
3D Computer Vision Engineer: These specialists interpret and reconstruct the three-dimensional world from visual data, developing algorithms for depth sensing and point cloud processing. Their work is crucial in VR, AR, and autonomous navigation systems.
Computer Vision Software Engineer: Responsible for building software infrastructure supporting computer vision systems, these engineers implement and maintain libraries and frameworks for deploying applications. They ensure scalability in consumer electronics and cloud-based platforms.
Computer Vision Product Engineer: Working closely with cross-functional teams, these engineers define product requirements and guide development processes. Their understanding of both technology and business strategies is essential in industries like retail analytics and healthcare diagnostics.

Skills Needed for a Computer Vision Engineer

Proficiency in machine learning and deep learning concepts, including statistical models and neural network architectures.
Adept at image and video processing techniques to manipulate and analyze visual data effectively.
Proficiency in programming languages like Python, C++, and Java for implementing computer vision algorithms and frameworks.
Familiarity with neural network architectures and frameworks such as TensorFlow and PyTorch.
Knowledge of computer vision libraries like OpenCV, PIL, and scikit-image.
Understanding 3D vision and geometry concepts for interpreting and reconstructing three-dimensional visual data.
Proficiency in data preprocessing, clustering, classification, and working with big data tools.
Strong mathematical skills in linear algebra, calculus, probability, and geometry.
Expertise in object detection, recognition, and tracking techniques for building applications like surveillance systems and autonomous vehicles.
Strong problem-solving and analytical skills to tackle complex challenges in visual data processing.
Attention to detail and precision for ensuring accuracy and reliability in computer vision applications.

Hire high-performing Computer Vision Engineers from a vetted talent network →

% change in demand for programming languages

4. Rust Developer

Salary Potential for Rust Developers according to ZipRecruiter: $170,000

Rust has maintained its position as the "most desired programming language" in Stack Overflow's developer survey for eight consecutive years. A Rust developer specializes in the Rust programming language, known for its memory-intensive applications like gaming engines, operating systems, and browsers. Rust can also be utilized for programming JavaScript engines and developing add-ons for Node.js. Rust developers excel in writing new code and maintaining existing Rust codebases for companies.

Top Use Cases for Rust

IoT: Rust's memory safety and low-level control make it ideal for developing embedded systems in Internet of Things devices with limited resources. Its ability to handle concurrency efficiently suits applications requiring multiple connections.
Embedded Systems: Rust's focus on memory safety and control makes it a preferred choice for embedded systems used in diverse fields like medical devices, aerospace, and automotive systems.
Robotics: Rust's features, including low-level control and memory safety, make it suitable for real-time processing in robotics applications. Its concurrency capabilities enable efficient handling of multiple threads, crucial for robotics operations.
Automobiles: Rust finds application in developing software for various car components such as engine control units, infotainment systems, and advanced driver assistance systems (ADAS).
Devices: Rust is used to develop software for a broad range of devices like cameras, smart home devices, and wearables, leveraging its efficiency and reliability.
AR/VR: Rust's concurrency features enable efficient handling of multiple threads, making it suitable for building robust and scalable AR/VR applications requiring real-time processing and responsiveness.

Skills Needed for a Rust Developer

Extensive experience with the Rust programming language, including its syntax, features, and best practices.
Knowledge of low-level backend programming languages like C/C++ to effectively handle system-level tasks and optimize performance.
Familiarity with JavaScript for integrating Rust with frontend technologies and developing full-stack applications.
Knowledge of kernel development tools such as WinDbg for low-level system programming and debugging.
Experience building enterprise-grade solutions for specialized platforms like Blockchain and AI.
Experience with secure coding practices to ensure the security and integrity of software applications.
Strong testing skills to implement comprehensive test suites and ensure the reliability of software products.
Strong collaboration and communication skills to work effectively in a team environment, collaborate with stakeholders, and communicate technical concepts clearly.

Hire high-performing Rust Developers from a vetted talent network →

5. Elixir Developer

Salary Potential for Elixir developers according to ZipRecruiter: $154,000

Elixir, a programming language running on the Erlang virtual machine, offers a robust framework for fault-tolerant coding and engineering, primarily focusing on web development and software applications. Renowned for its lightweight coding approach, Elixir comes equipped with built-in tools for running tests and remote debugging, making it a preferred choice for web-based applications.

A standout feature of Elixir lies in its ability to handle scalability, adeptly managing numerous users simultaneously. Depending on the organizational context, Elixir developers may find themselves tasked with various programming responsibilities, including code conversion from other applications to build innovative software solutions.

Elixir developer roles encompass a spectrum of programming tasks, often requiring expertise in complementary technologies such as MySQL, Redis, OTP, Jenkins, and Phoenix, among others.

Top Elixir Use Cases

Web Development: Elixir is a go-to choice for creating dynamic web applications, APIs, and web services, with its robust framework like Phoenix. Its unique concurrency model and fault-tolerant design empower developers to build highly scalable and resilient web solutions adept at managing heavy traffic and concurrent requests effortlessly.
Real-Time Communication: Elixir proves its mettle in developing real-time communication platforms, including chat systems, messaging apps, and multiplayer gaming environments.
Microservices Architecture: Thanks to its lightweight processes, message-driven communication, and supervision trees, Elixir is widely used for microservices architecture. This approach enables the creation of modular, independently deployable services, fostering scalability in application development.
Internet of Things (IoT): With its low-latency performance and real-time capabilities, Elixir empowers developers to process vast sensor data volumes, manage IoT device operations, and improve IoT workflows with utmost efficiency.

Skills Needed for an Elixir Developer

Ability to work with Elixir's concurrency primitives like GenServer, Task, and Flow for handling simultaneous data processing tasks.
Proficiency in designing and implementing concurrent data pipelines, batch processing workflows, and distributed computation algorithms.
Expertise in designing scalable and fault-tolerant web architectures using Phoenix and OTP (Open Telecom Platform).
Capability to build fault-tolerant and resilient systems using OTP behaviors such as supervisors, workers, and supervision trees.
Knowledge of designing and implementing distributed systems and microservices architectures.
Familiarity with profiling tools like ExProf and fprof to identify performance bottlenecks and apply code optimization, memory optimization, and concurrency tuning.

Hire high-performing Elixir Developers from a vetted talent network →

6. PyTorch Developer

PyTorch Developer Average Salary Potential according to ZipRecruiter: $151,833

PyTorch is a cutting-edge open-source deep learning framework crafted by Facebook's AI Research lab (FAIR). Known for its adaptability, dynamic computation graph, and user-friendly interface, PyTorch has emerged as a top choice for both researchers and practitioners in the fields of artificial intelligence (AI) and machine learning. From computer vision and natural language processing to reinforcement learning and more, PyTorch skills offer a gateway to innovation and transformative AI solutions.

Top Use Cases for PyTorch

Deep Learning Research: PyTorch is widely used in deep learning and artificial intelligence research, owing to its versatility, expressiveness, and dynamic computation graph. Researchers leverage PyTorch to prototype new models, experiment with innovative architectures, and delve into powerful techniques across diverse domains like computer vision, natural language processing, generative modeling, and reinforcement learning.
Model Development: PyTorch is instrumental in creating and training deep learning models for image classification, object detection, speech recognition, text generation, and sequence prediction. Developers rely on PyTorch's user-friendly APIs and extensive repository of pre-trained models to accelerate development cycles and achieve high-level performance on benchmark datasets.
Production Deployment: Equipped with tools and libraries for model optimization, PyTorch empowers the deployment of models across diverse hardware platforms (e.g., CPUs, GPUs, TPUs) and their smooth integration into existing software ecosystems and workflows.

Skills Needed for a PyTorch Developer

Proficiency in Python, including its syntax, data handling, object-oriented programming, and libraries.
Strong grasp of deep learning principles, including neural networks, CNNs, RNNs, attention mechanisms, and reinforcement learning.
Familiarity with PyTorch and its key components, such as tensors, autograd, modules, optimizers, and utilities.
Experience in developing and training deep learning models for diverse tasks like image classification, object detection, speech recognition, text generation, and sequence prediction.
Deployment knowledge of platforms like TorchServe, ONNX, and TensorFlow Serving.

Hire high-performing PyTorch engineer from a vetted talent network →

7. TensorFlow Developer

TensorFlow Developer Average Salary Potential according to ZipRecruiter: $136,000 – $196,500

TensorFlow, developed by Google, is a free machine learning framework. It's widely used for constructing and training machine learning models and neural networks. It's an in-demand tool aiding companies in creating and deploying AI models effectively. Its versatility makes it sought-after across various industries and job roles. Positions like machine learning engineer, deep learning engineer, AI research scientist, NLP engineer, data scientists and analysts, AI product manager, AI consultant, AI systems architect, and AI ethics and compliance analyst all benefit from TensorFlow's capabilities.

Top Use Cases for TensorFlow

Healthcare: In healthcare, TensorFlow and AI imaging technologies can improve the speed and accuracy of interpreting medical images. Mobile apps powered by TensorFlow can enable users to snap pictures of their skin for identifying potential issues. Automated billing tools for hospitals are another area where TensorFlow can prove beneficial.
Education: Virtual learning platforms leverage TensorFlow to filter inappropriate chat messages and create a customized curriculum for individual students. It also aids in assessing and grading students efficiently.
Social Media: Social media platforms utilize TensorFlow to rank posts based on user relevance and conduct sentiment analysis to optimize their brand image. Photo storage apps employ computer vision to organize photos and facilitate advanced searches.
Search Engines: TensorFlow aids search engines in understanding web page content through natural language processing (NLP) and analyzing user behavior data for ranking signals. Its machine learning capabilities assist in pattern detection to identify spam and duplicate content.
Retail: AI and TensorFlow facilitate retail businesses in forecasting demand and providing personalized recommendations to customers on e-commerce platforms.

Skills Needed for a TensorFlow Developer

Deep understanding of TensorFlow's libraries and tools, including TensorFlow 2.x, Keras for model building and training, and TensorFlow.js for web deployment.
Solid knowledge of machine learning algorithms, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.
Familiarity with TensorFlow Data Services (TFDS) and data augmentation techniques.
Experience in deploying TensorFlow models in various environments including TensorFlow Lite for mobile and TensorFlow Extended (TFX) for production pipelines.
In-depth understanding of deep learning concepts like backpropagation, dropout, and batch normalization is necessary for building effective models.
Strong analytical skills are required to break down complex problems and diagnose issues within TensorFlow models.
Clear and effective communication for conveying complex TensorFlow concepts to team members who may not have a deep understanding of machine learning.

Hire high-performing TensorFlow developer from a vetted talent network →

AI hiring before layoffs and after layoffs in big tech companies

Top Companies Hiring These Tech Skills

The leading companies seeking these tech skills are Apple, Amazon, NVIDIA, Meta, TikTok, Deloitte, and Ericsson Worldwide.

Furthermore, these skills are in high demand across a wide range of industries besides technology. Every company, regardless of its sector, is striving to adopt technology, thereby increasing the need for these talents.

Industries such as aerospace and defense, insurance, financial services, manufacturing, automotive, and semiconductors are particularly keen on acquiring professionals with these sought-after skills.

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