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Neural Network Jobs (NOW HIRING)

Cmu

RF Cyber Lab Intern

Pittsburgh, PA · On-site

$14.50 - $19.50/hr

Position Summary: We seek an intern with knowledge of temporal neural network design as well as the fundamentals of digital signal processing to help develop and refine novel detection and ...

Apple Inc.

Video Machine Learning Engineer

San Diego, CA · On-site

$139.50 - $258.10/hr

Design and develop novel machine learning algorithms and neural network architectures for video processing, compression, understanding, and enhancement. * Train, evaluate, and iterate on deep ...

femtoAI

Hardware Engineer

San Bruno, CA · On-site

$147K - $194K/yr

As a Hardware Engineer, you will join femtoAI's hardware team to help design and build our novel neural network accelerator. Working in a small, highly collaborative group, you will contribute ...

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Neural Network information

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$22K

$106.6K

$162.5K

How much do neural network jobs pay per year?

As of Aug 15, 2026, the average yearly pay for neural network in the United States is $106,570.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $128,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a neural network engineer, and why are they important?

To thrive as a Neural Network Engineer, you need a solid background in mathematics, machine learning theory, and programming, often backed by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience in data preprocessing, and knowledge of cloud computing platforms are typically required. Strong problem-solving abilities, collaboration, and effective communication skills distinguish top professionals in this role. These competencies are vital for developing, optimizing, and deploying neural network models that drive innovation in AI-powered solutions.

What is the difference between Neural Network vs Data Scientist?

AspectNeural NetworkData Scientist
Required CredentialsKnowledge of machine learning, programming skills, often a degree in computer science or related fieldsDegree in statistics, computer science, or related fields; strong analytical skills
Work EnvironmentResearch labs, tech companies, AI development teamsBusiness environments, consulting firms, research institutions
Industry UsageDeveloping AI models, deep learning applicationsData analysis, predictive modeling, business insights

Neural networks focus on building and training AI models using complex algorithms, while data scientists analyze data to extract insights and inform decisions. Both roles often collaborate but serve different functions within the AI and data analysis ecosystem.

What is the main job of a neural network?

A neural network is a type of machine learning model used to recognize patterns and make predictions based on data. In a job context, roles involving neural networks typically focus on designing, training, and optimizing these models using programming skills and tools like Python and TensorFlow. The main job is to develop systems that can learn from data to solve complex problems such as image recognition, natural language processing, and data analysis.

What are neural networks?

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They consist of interconnected layers of nodes, or 'neurons,' that process data and learn to make predictions or decisions based on input data. Neural networks are widely used in applications such as image recognition, natural language processing, and autonomous systems. Their ability to learn complex patterns makes them powerful tools for solving problems that are difficult to program explicitly.

What are some common challenges neural network engineers face when deploying models to production environments?

Neural network engineers often encounter challenges such as model optimization for efficient inference, managing hardware constraints, and ensuring scalability during deployment. Addressing issues like latency, memory usage, and compatibility with production infrastructure is crucial, especially when models are resource-intensive. Collaborating closely with DevOps and software engineering teams is common to streamline deployment pipelines, monitor model performance, and quickly resolve issues that arise post-launch.
More about Neural Network jobs

What states have the most Neural Network jobs?

States with the most job openings for Neural Network jobs include:

What job categories do people searching Neural Network jobs look for?

The top searched job categories for Neural Network jobs are:

Neural Network jobs near you
Infographic showing various Neural Network job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 11% Part Time, and 7% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $106,570 per year, or $51.2 per hour.

Senior Systems Engineer - Signal Processing, Algorithms and Characterization

Mythic

Palo Alto, CA • On-site

$150K - $275K/yr

Full-time

Re-posted 14 days ago

Job description

Mythic is building the future of AI computing with breakthrough analog technology that delivers 100× the performance of traditional digital systems at the same power and cost. This unlocks bigger, more capable models and faster, more responsive applications—whether in edge devices like drones, robotics, and sensors, or in cloud and data center environments. Our technology powers everything from large language models and CNNs to advanced signal processing, and is engineered to operate from –40 °C to +125 °C, making it ideal for industrial, automotive, aerospace, and defense. We’ve raised over $100M from world-class investors including Softbank, Threshold Ventures, Lux Capital, and DCVC, and secured multi-million-dollar customer contracts across multiple markets.

Mythic's analog compute hardware is a massive integration of analog and digital components on a single chip, with multiple cascaded digital and analog stages. The fundamental compute atomic is a vector-product that is entirely processed in the analog domain. So, analog impairments like ADC/DAC non-linearity and weight-noise directly impact the accuracy of the vector-multiply operation. Mitigation techniques for these impairments thus become critical and is the realm that the Systems Engineer operates in. Therefore, the Systems Engineer role maps directly onto skills from RF baseband, high-speed digital communication system design and RF sensing— think Wi-Fi, SerDes, gigabit Ethernet and sensor signal processing. 

The Systems Engineering team

  • Sits at the intersection of Analog, AI, Firmware and Silicon Productization,

  • Models analog effects and their impact on neural network performance.

  • Develops signal-processing based solutions to mitigate impact of analog impairments on neural network accuracy

  • Works cross-functionally to validate, debug, and optimize analog compute hardware.

  • Contributes to the design of next-generation hardware.

  • Brings up new silicon, characterizes silicon performance and develops effective approaches for silicon screening

  • Builds frameworks for large-scale data capture and statistical error analysis for analog compute in the simulation domain and on actual silicon hardware

Here's what you will do

  • Own various aspects of algorithms and DSP blocks that optimize the performance of Mythic’s unique analog compute-in-memory technology from concept to customer deployment. This includes calibration loops, non-linearity compensation, offset-correction and estimation of residual-errors.

  • Work with model-training, compiler and firmware teams to productize these algorithms.

  • Write and modify firmware as needed to productize/debug algorithms

  • Continually improve on the fidelity of our modeling and simulation environment to better predict silicon performance.

  • Correlate errors seen on silicon to simulation models and contribute to improving the fidelity of our models for analog compute.

  • Develop Python frameworks for data collection, error-analysis and quantify impact of analog impairments on neural-network accuracy

  • Silicon bring-up, Characterization and Performance-Optimization.

Here's the background you need to have

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Mathematics, Physics or a related field.

  • At least 5 years experience in production DSP or RF baseband engineering (< 3 years if Ph.D or M.S.)

  • Strong familiarity with production Python coding, including object oriented and/or functional programming

  • Strong familiarity with core DSP concepts, including frequency domain analysis, filtering, statistical signal processing and estimation theory

  • Track record of shipping silicon with DSP or RF/Analog sub-systems. 

  • Understanding of linear algebra concepts, including matrix math and linear regression. 

  • Comfort with large-scale collection and processing of signals. 

  • Commitment to quality and engineering excellence.

  • Strong communication skills.

The following would be nice to have

  • MS/PhD in Electrical Engineering, Computer Science, Mathematics, Physics or related field.

  • Experience with RF calibration and silicon-bringup in the high-speed communication space

  • Strong familiarity with NumPy/SciPy (or experience with Numpy and strong familiarity with MATLAB for DSP).

  • Familiarity with state-of-the-art neural network architectures

Compensation is based on a variety of factors, including but not limited to: location, education, and years of experience.
At Mythic, we pride ourselves in creating a culture where all employees feel valued and appreciated for the diverse perspectives and backgrounds they bring to the team. We aim to hire smart people, give them the resources they need to do their job well, and then leave the rest up to them. We celebrate individual differences and encourage people to be comfortable bringing their authentic selves to work. At the end of the day, we are committed to building a diverse workforce where everyone belongs.

Mythic is an equal opportunity and affirmative action employer. It ensures equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, veteran status, or any other characteristic protected by law.

We look forward to reviewing your application!