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Neural Networks Jobs in Wisconsin (NOW HIRING)

Serve Robotics

$225K - $260K/yr

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 ...

Next Gen Alloys

Data Scientist

Cudahy, WI

Knowledge of image segmentation, generative models, convolutional neural networks. * Experience in applied machine learning. Preferred Qualifications (in addition to Basic Qualifications)

ATI

Data Scientist

Cudahy, WI · On-site

Knowledge of image segmentation, generative models, convolutional neural networks. * Experience in applied machine learning. Preferred Qualifications (in addition to Basic Qualifications)

ATI

Data Scientist

Cudahy, WI

Knowledge of image segmentation, generative models, convolutional neural networks. * Experience in applied machine learning. Preferred Qualifications (in addition to Basic Qualifications)

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$27.4K

$120.9K

$183.1K

How much do neural networks jobs pay per year?

As of Jul 27, 2026, the average yearly pay for neural networks in Wisconsin is $120,890.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,357.00 and $153,406.00 per year, depending on experience, location, and employer.

What are the most common challenges faced in a Neural Networks role, and how can I prepare for them?

Professionals working in neural networks frequently encounter challenges such as managing large datasets, tuning hyperparameters, handling overfitting or underfitting, and keeping up with rapidly evolving technologies. You can prepare by building a strong foundation in relevant mathematical concepts, staying up-to-date on industry advancements, and practicing hands-on model development and troubleshooting. Collaborating with peers and participating in open-source projects or competitions can deepen your expertise and problem-solving skills. Employers also value candidates who can communicate complex ideas clearly and work well in diverse, multidisciplinary teams.

What is a Neural Networks job?

A Neural Networks job typically involves designing, developing, and optimizing artificial neural networks for tasks such as image recognition, natural language processing, and predictive analytics. Professionals in this field work with machine learning frameworks like TensorFlow or PyTorch, train deep learning models, and fine-tune architectures for better accuracy and efficiency. These roles are common in AI research, data science, robotics, and software development. Strong skills in programming, mathematics, and data handling are essential for success in this field.

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

To thrive in a Neural Networks role, you need a solid background in mathematics, programming (Python, TensorFlow, PyTorch), and machine learning principles, often attained through a degree in computer science or a related field. Familiarity with neural network frameworks, model deployment tools, and cloud computing platforms is highly valuable, as are certifications such as TensorFlow Developer or AWS Machine Learning. Excellent problem-solving abilities, communication skills, and a collaborative mindset help you excel when working on interdisciplinary teams and complex projects. These skills are crucial for designing, training, and optimizing neural network models that effectively solve real-world problems in diverse industries.

What are popular job titles related to Neural Networks jobs in Wisconsin? For Neural Networks jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Neural Networks jobs in Wisconsin look for? The top searched job categories for Neural Networks jobs in Wisconsin are:
Neural Networks jobs near you
Infographic showing various Neural Networks job openings in Wisconsin as of July 2026, with employment types broken down into 84% Full Time, and 16% Contract. Highlights an 82% In-person, and 18% Remote job distribution, with an average salary of $120,890 per year, or $58.1 per hour.
Lead Machine Learning Engineer

Lead Machine Learning Engineer

Serve Robotics

On-site, Remote

$225K - $260K/yr

Full-time

Posted yesterday

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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