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Neural Network Engineer Jobs (NOW HIRING)

Avride

Senior / Staff Machine Learning Engineer

Austin, TX · On-site

$124K - $171K/yr

Required : • Strong understanding of fundamental machine learning algorithms and neural network ... Avride is a developer and operator of autonomous vehicles and delivery robots. Founded in 2017, the ...

Avride

Machine Learning Engineer

Austin, TX

About the role We are looking for an experienced Machine Learning Engineer with a strong background ... At least three years of experience developing neural network-based algorithms, including data ...

Nvidia

$54.50 - $72/hr

Enhance tool support to improve deep neural network design and performance efficiency. * Partner with management and architects to translate requirements into designs and own the development. * Stay ...

Avride

Senior / Staff Machine Learning Engineer

Austin, TX · On-site

$124K - $171K/yr

Senior: 4+ years of experience developing neural network-based algorithms, including data ... engineering practices beyond your immediate team. * Principal: 10+ years of experience, with ...

Polaris, Inc.

Systems Engineer

Huntsville, AL · On-site

Support and maintain existing neural network and vision system customizations within the ... Bachelor's degree in Engineering, Information Technology, Computer Science, Software Engineering ...

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Neural Network Engineer information

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How much do neural network engineer jobs pay per year?

As of Jul 11, 2026, the average yearly pay for neural network engineer in the United States is $109,040.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,000.00 and $133,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Neural Network Engineer position, and why are they important?

To thrive as a Neural Network Engineer, you need a strong background in machine learning, deep learning frameworks (such as TensorFlow or PyTorch), and proficiency in programming languages like Python or C++. Experience with GPU computing, cloud-based machine learning platforms, and relevant certifications (e.g., TensorFlow Developer Certificate) is often valuable. Strong problem-solving skills, teamwork, and effective communication help you excel when collaborating on complex AI models and projects. These abilities are essential for designing effective neural networks, integrating them into products, and driving innovation in real-world applications.

What are the common daily responsibilities of a Neural Network Engineer?

On a typical day, a Neural Network Engineer may design and test deep learning model architectures, preprocess data, write and optimize code, and analyze performance results. Collaborating closely with data scientists, software engineers, and product managers is common to align model development with business objectives. Engineers often participate in code reviews, debugging sessions, and contribute to technical documentation. Staying current with the latest research and integrating new approaches is also part of the role, ensuring that solutions are both cutting-edge and practical for deployment.

What does a Neural Network Engineer do?

A Neural Network Engineer designs, develops, and optimizes machine learning models, particularly artificial neural networks, to solve complex problems. They work with deep learning frameworks like TensorFlow and PyTorch, train and fine-tune models, and optimize them for performance and efficiency. Their role often involves preprocessing data, selecting appropriate architectures, and deploying models in real-world applications such as computer vision, natural language processing, or autonomous systems.

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Neural Network Engineer jobs near you
Infographic showing various Neural Network Engineer job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 94% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $109,040 per year, or $52.4 per hour.
Senior Systems Engineer - Signal Processing, Algorithms and Characterization

Senior Systems Engineer - Signal Processing, Algorithms and Characterization

Mythic

Palo Alto, CA

$122K - $168K/yr

Full-time

Posted 9 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

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!
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