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Neural Engineering Jobs in Wisconsin (NOW HIRING)

Serve Robotics

$225K - $260K/yr

By optimizing distributed training pipelines, neural network architectures, and data processing ... Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a ...

Next Gen Alloys

Data Scientist

Cudahy, WI

... and programming background. * Experience in deep learning, predictive modeling, data mining, and time series analysis. * Knowledge of image segmentation, generative models, convolutional neural ...

ATI

Data Scientist

Cudahy, WI · On-site

... and programming background. * Experience in deep learning, predictive modeling, data mining, and time series analysis. * Knowledge of image segmentation, generative models, convolutional neural ...

ATI

Data Scientist

Cudahy, WI · On-site

... and programming background. * Experience in deep learning, predictive modeling, data mining, and time series analysis. * Knowledge of image segmentation, generative models, convolutional neural ...

The Weather Channel

Senior Data Scientist

Madison, WI · On-site

Experience using/implementing non-parametric regression such as Neural Net, SVM, Random Forest ... Programming capabilities including C++, Java, Python is a plus but not necessary. Additional ...

Wisconsin

Research Specialist

Madison, WI

These experiments will test hypotheses about the neural bases of visual cognition in humans, with ... Additional duties may include programming experimental tasks and carrying out preprocessing and ...

Varsity Tutors

Machine Learning Tutor

Milwaukee, WI · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... neural network architectures while preparing students for data science roles and advanced AI ...

Varsity Tutors

Machine Learning Tutor

Madison, WI · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... neural network architectures while preparing students for data science roles and advanced AI ...

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All JobsNeural Engineering JobsNeural Engineering Jobs in Wisconsin

Neural Engineering information

See Wisconsin salary details

$11

$19

$29

How much do neural engineering jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for neural engineering in Wisconsin is $19.50, according to ZipRecruiter salary data. Most workers in this role earn between $16.25 and $21.11 per hour, depending on experience, location, and employer.

What is neural engineering?

Neural engineering is a multidisciplinary field that combines engineering, neuroscience, and computational approaches to understand, repair, enhance, or interface with the nervous system. Neural engineers develop devices such as brain-computer interfaces, neural prosthetics, and neurostimulation systems to restore or improve neural function. This field plays an important role in advancing treatments for neurological disorders and in creating technologies that bridge the gap between machines and the human brain.

What are the key skills and qualifications needed to thrive as a Neural Engineer, and why are they important?

To thrive as a Neural Engineer, you need a strong background in neuroscience, biomedical engineering, and signal processing, typically supported by an advanced degree in a related field. Familiarity with programming languages (such as MATLAB or Python), neuroimaging tools, and hardware platforms used for neural interfacing is essential. Excellent problem-solving skills, collaboration, and clear communication set standout professionals apart in this multidisciplinary environment. These skills are crucial for developing innovative neural technologies and translating research into effective clinical or commercial solutions.

What Are Jobs in Neural Engineering?

Jobs in neural engineering focus on helping research and design biomedical devices like prosthetic limbs and artificial organs. In these roles, you may determine the best way to implement designs for each situation, figure out the best way to link mechanical systems to the human brain, and find the most cost-effective ways to build devices. Neural engineering differs from engineering regular prosthetic limbs in that they receive instructions directly from the brain and often send information back, rather than simply being attached to the body. This often involves programming specialized software and figuring out how to make devices that can teach the brain how to use them. In recent years, neural engineering has started to move out of the medical realm, and there may be more jobs of that nature in the future. Neural engineering is a specific type of biomedical engineering, but should not be confused with jobs in the broader category.

What are some common interdisciplinary challenges faced by neural engineers when collaborating with clinicians and data scientists?

Neural engineers frequently work on teams that include clinicians, data scientists, and hardware specialists, which can present unique interdisciplinary challenges. Effective communication is essential, as team members often have different technical backgrounds and priorities—clinicians focus on patient outcomes, while data scientists emphasize analytical accuracy. Bridging the gap between clinical needs and technical feasibility requires adaptability, openness to feedback, and a willingness to learn new concepts. Building strong collaborative relationships and participating in regular cross-functional meetings can help ensure that project goals are clearly understood and met by all stakeholders.
More about Neural Engineering jobs
What are popular job titles related to Neural Engineering jobs in Wisconsin? For Neural Engineering jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Neural Engineering jobs in Wisconsin look for? The top searched job categories for Neural Engineering jobs in Wisconsin are:
Neural Engineering jobs near you
Infographic showing various Neural Engineering job openings in Wisconsin as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $40,550 per year, or $19.5 per hour.
Lead Machine Learning Engineer

Lead Machine Learning Engineer

Serve Robotics

On-site, Remote

$225K - $260K/yr

Full-time

Re-posted 20 days ago

Job description

At Serve Robotics, we're reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It's designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We're looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.
Who We Are
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them.
Responsibilities
  • Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters.
  • Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time.
  • Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data.
  • Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance.
  • Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage.
  • Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training.
  • Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline.
  • Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies.
  • Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.

Qualifications
  • Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
  • Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments.
  • Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling.
  • Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows.
  • Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.

What Makes You Stand out
  • Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems.
  • Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data.
  • Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system.
  • Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas.

*Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada
  • Canada - ALL: $177k - $215k CAD

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