Applied Machine Learning Engineer II - Advanced Engineering & TechnologyMilwaukee Electric Tool Corporation • United States
Applied Machine Learning Engineer II - Advanced Engineering & Technology
Milwaukee Electric Tool Corporation
- United States
- United States
Über
* FullStack ML in a Physical Domain: Work across the ML stack, from machine and sensorlevel data through model deployment on edge hardware or cloud infrastructure. * R&D Engineering First: Apply ML across Technology Readiness Levels (TRL 1-7), bringing technology innovation to life beyond model tuning. Domain knowledge in materials, mechanics, signals, or physics is central to this role. * Flexible Tools: Select and use frameworks and libraries best suited to the problem, without being constrained to a single ecosystem. * Real Impact: Deliver MLdriven capabilities that shorten product development cycles and unlock new engineering possibilities at Milwaukee Tool.What You'll Do:
* 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 physicsbased or analytical approaches and defining practical success criteria. * Design, train, evaluate, and deploy ML models to solve applied science and engineering problems that expand product development capabilities. * Build endtoend ML workflows spanning data acquisition, feature engineering, model development, validation, and deployment (PyTorch, TensorFlow, CUDA, Azure ML). * 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. * Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs. * Identify and assess emerging technologies via literature, universities, conferences, and vendor engagement.What You'll Bring:Required
* 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. * +3 or more years of experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar). * Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems. * Strong working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikitlearn), with working knowledge of SQL. * Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow) and familiarity with 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. * Demonstrated ability to formulate ambiguous engineering or scientific problems into well-defined ML problems with clear objectives and evaluation criteria.
* Curiositydriven approach to learning new technologies and methods, with emphasis on applying machine learning to realworld 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 or PhD in relevant field. * Familiarity with physics-informed ML approaches, embedding physical constraints in model architecture, or surrogate modeling for simulation acceleration. * Experience with computer vision for engineering applications. * Exposure to edge deployment: model optimization containerized deployment to industrial hardware. * Experience with design of experiments (DOE), uncertainty quantification, or Bayesian optimization. * Familiarity with version control, experiment tracking, and reproducible research practicesWorking Environment
* In-Person, Office Environment, R&D Engineering LabOur Perks and Benefits:
* Robust health, dental and vision insurance plans * Generous 401 (K) savings plan * Education assistance * On-site wellness, fitness center, food, and coffee service * And many more, check out our benefits site.
Milwaukee Tool is an equal opportunity employer.
Sprachkenntnisse
- English
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