5 months ago
Brookfield, WI, USAMid Level
Responsibilities
- Research and evaluate emerging AI and machine-learning technologies through the TRL 1–7 process.
- Assess whether engineering problems are best addressed with machine learning, physics-based methods, or analytical approaches, and define success criteria.
- Design, train, evaluate, and deploy ML models for applied science and engineering problems.
- Build end-to-end ML workflows covering data acquisition, feature engineering, model development, validation, and deployment.
- Deploy ML-enabled systems on edge hardware and cloud infrastructure.
- Document architecture decisions, limitations, data requirements, and deployment specifications for technology transfer.
- Collaborate with engineering and cross-functional teams to deliver ML solutions.
- Identify emerging technologies through literature, universities, conferences, and vendor engagement.
- Travel approximately 10% domestically and internationally.
Requirements
- Bachelor’s degree in mechanical engineering, electrical engineering, materials science, physics, computer science, data science, or a related engineering discipline, with advanced ML coursework or experience.
- At least 3 years of experience applying machine learning to physical-world engineering or scientific problems.
- Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems.
- Strong working knowledge of Python, NumPy, SciPy, Pandas, scikit-learn, and SQL.
- Hands-on experience with PyTorch or TensorFlow and familiarity with Azure ML, AWS SageMaker, or an equivalent cloud ML platform.
- Strong foundations in linear algebra, probability, statistics, and optimization, including loss functions, convergence behavior, and model assumptions.
- Ability to formulate ambiguous engineering or scientific problems as well-defined ML problems with clear objectives and evaluation criteria.
- Ability to work across diverse data types and evaluate technologies collaboratively in an ambiguous, fast-paced environment.
- Preferred qualifications include a master’s degree or PhD, physics-informed ML, physical constraints in model architectures, surrogate modeling, engineering computer vision, edge deployment, model optimization, containerized deployment to industrial hardware, design of experiments, uncertainty quantification, Bayesian optimization, version control, experiment tracking, and reproducible research practices.
Benefits
- Health, dental, and vision insurance plans.
- 401(k) savings plan.
- Education assistance.
- On-site wellness services, fitness center, food, and coffee service.
- In-person work in an office environment and R&D engineering lab.
- Approximately 10% domestic and international travel.
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