2 months ago
Brookfield, WI, USAEntry Level
Responsibilities
- Research and evaluate emerging AI and ML technologies and advance them through the Technology Readiness Level process from concept through integration.
- Frame engineering problems as ML problems and assess ML approaches against physics-based or analytical methods.
- Design, train, evaluate, debug, and validate ML models for applied science and engineering problems.
- Build data workflows covering acquisition, organization, cleaning, feature engineering, visualization, model development, and validation.
- Support deployment of ML-enabled systems on edge hardware and cloud infrastructure.
- Prepare technology-transfer packages documenting architecture decisions, limitations, data requirements, and deployment specifications.
- Conduct experiments, analyze data, support data-collection and test plans, and collaborate with cross-functional engineering teams.
- Research emerging technologies through literature, universities, conferences, and vendor engagement.
Requirements
- Bachelor’s degree in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or a related engineering discipline, with advanced machine-learning coursework or experience.
- Experience applying machine learning to physical-world engineering or scientific problems.
- Demonstrated experience designing, training, and evaluating ML models on real-world or academic problems.
- Working knowledge of Python and scientific computing with NumPy, SciPy, Pandas, and scikit-learn, plus familiarity with SQL.
- Exposure to PyTorch or TensorFlow, including model training, and awareness of Azure ML, AWS SageMaker, or equivalent cloud ML platforms.
- Strong foundations in linear algebra, probability, statistics, and optimization, including understanding of loss functions, convergence behavior, and model assumptions.
- Ability to formulate well-scoped engineering or scientific tasks as ML problems with clear objectives and evaluation criteria.
- Ability to work with diverse data types, collaborate hands-on, learn new technologies, and operate in an ambiguous, fast-paced environment.
- Ability to travel 10% of the time domestically and internationally.
- Preferred: master’s degree, sensor and physical-data interpretation, engineering test-lab experience, computer vision, edge deployment and model optimization, containerized deployment, design of experiments, uncertainty quantification, Bayesian optimization, version control, experiment tracking, and reproducible research practices.
Benefits
- In-person work in an office and R&D engineering lab environment.
- Domestic and international travel of approximately 10%.
- Health, dental, and vision insurance.
- 401(k) savings plan.
- Education assistance.
- On-site wellness services, fitness center, food, and coffee service.
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