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Machine Learning Engineer Intern Jobs in Kansas (NOW HIRING)

Overstory

Role & Team As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation ...

Ascend Learning

Software Engineer Intern

Leawood, KS · On-site

From testing to certification, Ascend Learning products are used by physicians, emergency medical ... WHAT YOU'LL DO The Intern Software Engineer will have responsibility for developing, testing ...

PICTOR LABS INC

$89K - $123K/yr

About the Role We are seeking an experienced Senior ML Inference Engineer to join our team, focusing on optimizing and deploying our production virtual staining models at scale. The ideal candidate ...

StackAdapt

We are searching for a talented Senior/Staff Applied Machine Learning Scientist to join our engineering team as we continue to expand our data science efforts. Our platform is connected to thousands ...

Autodesk

$131K - $235K/yr

As a Senior Machine LearningEngineer focused on Machine Learning Ops (MLOps) for CAD and BIM, you ... You will partner closely with researchers, evaluation engineers, and product teams to translate ...

Varsity Tutors

Machine Learning Tutor

Wichita, KS · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Overland Park, KS · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Machine Learning Engineer Intern information

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How much do machine learning engineer intern jobs pay per year?

As of Jul 9, 2026, the average yearly pay for machine learning engineer intern in Kansas is $37,978.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,000.00 and $41,000.00 per year, depending on experience, location, and employer.

What types of projects and tasks do Machine Learning Engineer Interns typically work on?

Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.

What is a Machine Learning Engineer Intern job?

A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.

What are the key skills and qualifications needed to thrive in the Machine Learning Engineer Intern position, and why are they important?

To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

What are the most commonly searched types of Machine Learning Engineer jobs in Kansas? The most popular types of Machine Learning Engineer jobs in Kansas are:
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Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Kansas as of July 2026, with employment types broken down into 96% Full Time, 2% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $37,978 per year, or $18.3 per hour.

Staff Machine Learning Engineer - Wildfire

Overstory

On-site, Remote

Other

Re-posted 3 days ago

Job description

Role & Team

As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation structure, fuel loads, and wildfire risk from satellite and environmental data. You'll help shape the next generation of Overstory's modeling capabilities by combining cutting-edge ML techniques, large-scale geospatial data, and real-world domain expertise.

Reporting to our VP of Product Engineering, you'll work closely with data scientists, ML engineers, and product teams to ensure our wildfire models are accurate, robust, and production-ready - balancing scientific rigor with practical engineering excellence. As a senior technical leader, you'll mentor other engineers, drive architectural decisions, and define standards for modeling, experimentation, and deployment across Overstory.

Time zone requirement: Eastern North America (NST, AST, EST)

What You'll Do

In collaboration with data, ML, and science colleagues, you will:

  • Architect and build advanced ML models to map and predict vegetation and fuel conditions across diverse geographies.
  • Design and maintain robust data and feature pipelines for large-scale geospatial and temporal data.
  • Partner with wildfire science and product teams to define modeling objectives and evaluation metrics tied to real-world impact.
  • Build reproducible experimentation frameworks and model evaluation workflows.
  • Scale models from research to production with a focus on performance, reliability, and explainability.
  • Lead the evolution of ML systems, tooling, and processes - ensuring that our wildfire fuelscape models remain state-of-the-art and maintainable.
  • Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments.
Skills & Experience
  • Experience thriving at the intersection of machine learning, geospatial data, and environmental science; deeply motivated by the opportunity to reduce wildfire risk through data-driven insights
  • 10+ years of experience designing and building production-grade ML pipelines and systems 
  • Strong background in deep learning, computer vision, or remote sensing
  • Skilled in designing end-to-end ML systems - from data ingestion and preprocessing to deployment and monitoring
  • Hands-on experience with frameworks like PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas
  • Familiarity with GCP and Vertex AI, or similar cloud-based ML platforms
  • Strong communication skills and ability to collaborate across technical and scientific domains
  • Comfortable leading architectural discussions and mentoring other engineers
Nice To Have
  • Background in wildfire science, forestry, or remote sensing
  • Experience integrating physics-based models with ML or working with active learning and uncertainty quantification
  • Experience in model interpretability and data provenance for environmental ML systems
  • Experience with deep learning models for weather or climate data
  • Experience in remote-first or globally distributed teams

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer
  • Competitive, location-specific compensation and benefits 
  • Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
  • Home office stipend, coworking and ongoing education budgets 
  • A company culture that genuinely embodies each of our core values
  • To be part of truly mission-driven work that reduces wildfires, protects earth's natural resources and helps solve our climate crisis

Apply