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Machine Learning Software Engineer Intern Jobs in Wisconsin

$225K - $260K/yr

Who We Are We are tech industry veterans in software, hardware, and design who are pooling our ... Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a ...

Software Developer Intern

Antigo, WI · On-site

$20.75 - $27.25/hr

CoVantage Credit Union is seeking a Software Developer Intern. This individual will focus primarily on efforts related to software development, web development, backend/middle-tier development ...

Software Developer Intern

Antigo, WI · On-site

$20.75 - $27.25/hr

CoVantage Credit Union is seeking a Software Developer Intern. This individual will focus primarily on efforts related to software development, web development, backend/middle-tier development ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

As a Software Engineering Intern at Danaher, you'll work alongside experienced engineers developing diagnostic software that powers critical healthcare tools used across the globe. What You Might ...

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Machine Learning Software Engineer Intern information

What does a Machine Learning Software Engineer Intern do?

A Machine Learning Software Engineer Intern assists in the development, testing, and deployment of machine learning models and algorithms. Their responsibilities typically include data preprocessing, model training, evaluation, and collaborating with senior engineers to integrate machine learning solutions into software products. Interns may also contribute to research, documentation, and code optimization, gaining hands-on experience with real-world machine learning projects. This role provides a valuable opportunity to apply academic knowledge in a professional setting and learn from experienced engineers.

What are the key skills and qualifications needed to thrive as a Machine Learning Software Engineer Intern, and why are they important?

To thrive as a Machine Learning Software Engineer Intern, you need a solid understanding of programming (especially Python), machine learning algorithms, and data structures, ideally supported by coursework or relevant projects. Familiarity with frameworks such as TensorFlow or PyTorch, experience using version control systems like Git, and knowledge of cloud platforms are highly valuable. Critical thinking, eagerness to learn, and effective communication help interns collaborate with teams and adapt to new challenges. These skills and qualities are crucial for developing robust ML solutions, integrating with production systems, and contributing meaningfully to real-world projects.

What types of projects and responsibilities can a Machine Learning Software Engineer Intern expect during their internship?

As a Machine Learning Software Engineer Intern, you can expect to work on projects that involve data preprocessing, model development, and evaluation under the guidance of experienced engineers and data scientists. Interns often contribute to building and optimizing machine learning pipelines, implementing algorithms, and supporting the deployment of models into production environments. Collaboration is key; you'll likely work closely with cross-functional teams, including product managers and software developers, to ensure your solutions align with business goals. The internship is a great opportunity to gain hands-on experience with industry-standard tools and frameworks while receiving mentorship and feedback to help advance your technical skills.
What are the most commonly searched types of Machine Learning Software Engineer jobs in Wisconsin? The most popular types of Machine Learning Software Engineer jobs in Wisconsin are:
What cities in Wisconsin are hiring for Machine Learning Software Engineer Intern jobs? Cities in Wisconsin with the most Machine Learning Software Engineer Intern job openings:
Lead Machine Learning Engineer

Lead Machine Learning Engineer

Serve Robotics

On-site, Remote

$225K - $260K/yr

Full-time

Posted 12 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