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Machine Learning Engineer Opt Jobs in Wisconsin (NOW HIRING)

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

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Machine Learning Engineer Opt information

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What are popular job titles related to Machine Learning Engineer Opt jobs in Wisconsin? For Machine Learning Engineer Opt jobs in Wisconsin, the most frequently searched job titles are:

Machine Learning Engineer I

Milwaukee Tool

Brookfield, WI โ€ข On-site

Full-time

Re-posted 14 days ago


Job description

Job Summary:
Techtronic Industries - TTI is committed to innovation and excellence in engineering, particularly in the realm of power tools. As a Machine Learning Engineer I, you will lead the deployment of machine learning models, collaborating with cross-functional teams to enhance product development and implementation. This role emphasizes problem-solving, technical communication, and project management to drive initiatives that improve user experiences.
Responsibilities:
โ€ข Deploy machine learning models in creative ways while working with cross-functional teams.
โ€ข Act as a technical expert in the creation and execution of concepts into products.
โ€ข Support the team through implementation, validation, and transfer to production.
โ€ข Leverage strong technical communication skills and project management abilities to ensure clarity and alignment across teams.
โ€ข Demonstrate a strong sense of ownership for projects and tasks, understanding their connection to broader initiatives.
Qualifications:
Required:
โ€ข Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.
โ€ข Completed course work or specialization in Machine Learning and/or Data Science
โ€ข Demonstrated experience applying fundamental machine learning algorithms and techniques in a non-coursework setting (e.g. unsupervised or supervised learning, classification/regression, dimensionality reduction, model optimization)
โ€ข Demonstrated experience with machine learning and AI methods such as CNNS, transformers, or computer vision
โ€ข Proficient developing and debugging code in Python
โ€ข Proficiency in Python, with extensive experience in common libraries (NumPy, pandas, scikit-learn, Matplotlib, etc.)
โ€ข Proficiency with at least one deep learning framework (e.g. PyTorch of Tensor Flow)
โ€ข Sold mathematical foundation in statistics, linear algebra, calculus and optimization
โ€ข Ability to travel up to 10% of the time (domestic and international).
Preferred:
โ€ข Masterโ€™s degree or PhD in Machine Learning or related field
โ€ข At least one year of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field are preferred
โ€ข Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing
โ€ข Proficient developing and debugging code in an embedded environment in a programming language such as C or C++
โ€ข Experience working with modern software development tools and version control tools
Company:
Milwaukee Tool manufactures electric power tools and accessories. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .