> Markdown version of [/jobs/ext/1781438-applied-machine-learning-engineer-i-advanced](https://www.wearedevelopers.com/jobs/ext/1781438-applied-machine-learning-engineer-i-advanced). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Machine Learning Engineer I - Advanced... - **Company:** Techtronic Industries North America, Inc. - **Location:** Brookfield, WI, United States - **Experience:** Starter - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Cloud Computing, Software Debugging, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Azure Machine Learning, Scientific Computating, SciPy, SQL Databases, Feature Engineering, Pytorch, Deep Learning, Pandas, Scikit Learn, Information Technology, Machine Learning Operations, Software Version Control, Engineering Base - **Published:** July 15, 2026 - **Apply:** https://www.juju.com/job/00000000ggpw7n ## About the Role _Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time._, + BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning. + Experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar). + Demonstrated experience designing, training, and evaluating ML models on real-world or academic problems. + Working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit-learn), with familiarity with SQL. + Exposure to at least one deep learning framework (PyTorch or TensorFlow), including training models, and awareness of cloud ML platforms (Azure ML, AWS SageMaker, or equivalent). + Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions. + Ability to help formulate well-scoped engineering or scientific tasks into ML problems with clear objectives and evaluation criteria, and awareness of when different model classes should be used. + Curiosity-driven approach to learning new technologies and methods, with emphasis on applying machine learning to real-world scientific and engineering challenges. + Ability to work across a diverse range of data types. + Hands-on approach to collaboration and evaluation of technologies. + Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve. + Ability to travel 10% of the time (domestic and international). Preferred + Master's Degree in relevant field. + Familiarity with common sensors and interpreting their physical data, and exposure to engineering test lab workflows. + Experience with computer vision for engineering applications. + Awareness of edge deployment concepts: model optimization and containerized deployment to industrial hardware. + Coursework or exposure to design of experiments (DOE), uncertainty quantification, or Bayesian optimization. + Familiarity with version control, experiment tracking, and reproducible research practices ## Description + Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration. + Frame engineering problems as ML problems by assessing ML value versus physics-based or analytical approaches and defining practical success criteria. + Design, train, and evaluate ML models to help solve well-scoped applied science and engineering problems, working under the guidance of senior engineers. + Build ML workflows spanning data acquisition, feature engineering, model development, and validation using standard scientific and ML libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow). + Support algorithm selection and the construction of standard feature sets for engineering problems. + Support the deployment of ML models on edge hardware and cloud infrastructure, building and deploying with guidance. + Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions. + Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption. + Conduct experiments and data analysis following established patterns and methods; identify and debug basic model errors. + Organize, clean, and prepare data for downstream tasks, and create visualizations that support hypotheses, insights, and conclusions. + Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs, and support the design of data collection and test plans. + Research and learn about emerging AI and ML technologies through literature, universities, conferences, and vendor engagement. ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)