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Machine Learning Internship Opt Cpt Jobs in Ohio

Internship, academic project, or personal project experience in machine learning * Familiarity with Git and version control * Exposure to cloud platforms (AWS, Azure, or Google Cloud) * Basic ...

Internship, academic project, or personal project experience in machine learning * Familiarity with Git and version control * Exposure to cloud platforms (AWS, Azure, or Google Cloud) * Basic ...

New

Internship, academic project, or personal project experience in machine learning * Familiarity with Git and version control * Exposure to cloud platforms (AWS, Azure, or Google Cloud) * Basic ...

New

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Previous software engineering experience via an internship, work experience, or coding competition

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Previous software engineering experience via an internship, work experience, or coding competition

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Machine Learning Internship Opt Cpt information

What kinds of projects or tasks can a machine learning intern expect to work on during an internship?

As a Machine Learning intern, you can expect to be involved in projects such as data preprocessing, exploratory data analysis, model building, and performance evaluation. Interns often work with real datasets, contribute to feature engineering, and assist in deploying models or creating proof-of-concept solutions. Collaboration with data scientists, engineers, and sometimes product teams is common, providing valuable insights into real-world machine learning workflows. This hands-on experience helps interns build technical skills and gain exposure to best practices in the field.

What is a machine learning internship OPT CPT?

A Machine Learning Internship OPT CPT refers to an internship opportunity in the field of machine learning that is specifically available to international students in the U.S. on F-1 visas, who are eligible for Optional Practical Training (OPT) or Curricular Practical Training (CPT). These internships allow students to gain hands-on experience applying machine learning techniques in real-world projects while meeting their academic requirements or career goals. The roles typically involve tasks such as data analysis, building predictive models, and working with large datasets using programming languages like Python or R. OPT is usually used after graduation, while CPT is often part of the academic curriculum during the degree program.

What is the difference between Machine Learning Internship Opt Cpt vs Data Science Internship?

AspectMachine Learning Internship Opt CptData Science Internship
Required CredentialsTypically requires coursework or experience in machine learning, programming, and statisticsRequires knowledge in statistics, data analysis, and programming, often with a focus on data manipulation
Work EnvironmentTech companies, research labs, startups focusing on AI/ML projectsBroad industry sectors including finance, healthcare, tech, with data analysis focus
Employer & Industry UsageUsed by companies developing AI/ML products and servicesUsed across industries for data-driven decision making

While both internships involve working with data, the Machine Learning Internship Opt Cpt focuses on developing algorithms and models, whereas Data Science Internships emphasize data analysis and insights. Your choice depends on whether you want to specialize in machine learning techniques or broader data analysis tasks.

What are the key skills and qualifications needed to thrive as a machine learning intern OPT CPT?

To thrive as a Machine Learning Intern (OPT CPT), you need a solid background in computer science, mathematics, and statistics, often demonstrated by coursework or a related degree. Experience with programming languages like Python or R, familiarity with machine learning frameworks such as TensorFlow or scikit-learn, and understanding of data analysis tools are commonly required. Strong problem-solving abilities, curiosity, and effective teamwork and communication skills help interns stand out. These skills are crucial for successfully contributing to projects, learning from real-world data, and collaborating with multidisciplinary teams.
What job categories do people searching Machine Learning Internship Opt Cpt jobs in Ohio look for? The top searched job categories for Machine Learning Internship Opt Cpt jobs in Ohio are:
What cities in Ohio are hiring for Machine Learning Internship Opt Cpt jobs? Cities in Ohio with the most Machine Learning Internship Opt Cpt job openings:

Machine Learning Engineer

Apex Informatics

Cincinnati, OH โ€ข On-site

Full-time

Re-posted 27 days ago


Job description

Below is my newest requirement. Please send Full Legal Name, LinkedIn, Location, Contact Details, C2C rate, and work authorization status with each submittal.
Client: Kroger
Location: Hybrid onsite in Cincinnati OH (local only)
Interview Mode: Virtual Interview
Type: Contract
Work authorization: Cannot work with OPT or CPT
Rate: Open (market rate)
We are seeking a dynamic Senior Machine Learning Engineer to lead the integration and operationalization of machine learning models. This role requires collaboration with data scientists and leadership teams, and a strong foundation in MLOps methodologies. Experience in diverse ML platforms, including Google Vertex AI and other cloud and open-source technologies, is essential. The candidate will bridge MLOps, data science, and leadership to ensure the smooth functioning of our ML infrastructure.
Qualifications:
Minimum of 4 years of experience in MLOps, with a demonstrated ability to work with various ML platforms.
Strong proficiency in Python and familiarity with data science methodologies.
Experience with cloud technologies, particularly Google Cloud and Vertex AI, and adaptability to technologies like Microsoft Azure or open-source tools.
Excellent communication skills, capable of bridging technical and business domains
Experience in developing state-of-the-art techniques for multi-stage, personalized, context-aware, and sequential recommender systems.
Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, transformers, and LLMs.
Capable software engineering skills to lead a multi stage recommender system model lifecycle from inception to production.