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Research Machine Learning Federated Learning Jobs in Cincinnati, OH

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Senior Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

We are seeking an experienced Machine Learning Engineer with a strong bias for action, an ownership mindset, and a passion for solving complex business problems through automation and AI. The ideal ...

The perfect candidate brings a powerful blend of programming prowess, machine learning mastery, and operations research skillfulness, along with the rare ability to translate complex technical ...

... research and mentor team on emerging learning technologies, methodologies, and support systems resulting in the most effective and efficient solutions. o Identify new potential internal and external ...

They will gain an understanding of the retail business by learning and completing skill level ... Gain experience and exposure to hand sewing and machine sewing projects. * Experience the art of ...

They will gain an understanding of the retail business by learning and completing skill level ... Gain experience and exposure to hand sewing and machine sewing projects. * Experience the art of ...

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Research Machine Learning Federated Learning information

See Cincinnati, OH salary details

$24.5K

$40.9K

$84.4K

How much do research machine learning federated learning jobs pay per year?

As of Aug 23, 2026, the average yearly pay for research machine learning federated learning in Cincinnati, OH is $40,858.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,200.00 and $44,100.00 per year, depending on experience, location, and employer.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Cincinnati, OH?

For Research Machine Learning Federated Learning jobs in Cincinnati, OH, the most frequently searched job titles are:

What job categories do people searching Research Machine Learning Federated Learning jobs in Cincinnati, OH look for?

The top searched job categories for Research Machine Learning Federated Learning jobs in Cincinnati, OH are:

What cities near Cincinnati, OH are hiring for Research Machine Learning Federated Learning jobs?

Cities near Cincinnati, OH with the most Research Machine Learning Federated Learning job openings:

Infographic showing various Research Machine Learning Federated Learning job openings in Cincinnati, OH as of August 2026, with employment types broken down into 80% Full Time, and 20% Contract. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $40,858 per year, or $19.6 per hour.

Director, Machine Learning-Artificial Intelligence

Archer Daniels Midland

Erlanger, KY

Full-time

Posted 3 days ago

New


ADM rating

7.9

Company rating: 7.9 out of 10

Based on 186 frontline employees who took The Breakroom Quiz

75th of 365 rated logistics


Job description

Director, Machine Learning-Artificial Intelligence, Archer Daniels Midland Company, Erlanger, KY.
Provide guidance and hands-on development of machine learning solutions supporting data science
initiatives leveraging the Azure ecosystem and partner integrations. Research, design, implement, and
deploy full-stack scalable artificial intelligence and machine learning solutions to address various business
challenges. Design and develop immersive and interactive AR, VR, and XR applications that meet project
specifications and user requirements, while ensuring high performance, scalability, and usability. Design
and develop Generative AI solutions using fine-tuning, embeddings, with commercial and open-source
large language models. Lead the development of artificial intelligence initiatives in an environment rich in
complex biological, environmental, operational, and business data. Establish and manage collaborations
engaging business units to develop novel data analytic approaches and integrated decision science
solutions. Evaluate in-depth market analysis and drive the business case for optimization of existing
products and creation of next generation AI products. Support the development of AI product roadmaps
and timelines.
40 hrs/week, Mon-Fri, 8:30 a.m. - 5:30 p.m.
MINIMUM REQUIREMENTS:
Bachelor's degree or foreign equivalent degree in Machine Learning, Data Science, Statistics, Applied
Mathematics, Physics, Computer Science, Bioinformatics, Business Analytics, or a related field, and seven
(7) years of post-bachelor's, progressive, related work experience.
Must have five (5) years of experience with/in:
Applying artificial intelligence, machine learning, and architecture skills to solve commercial,
financial, and manufacturing problems;
Machine learning techniques (e.g., clustering, decision tree learning, and/or artificial neural
networks) and Machine Learning lifecycle from beginning to end;
Working with enterprise scale databases, with demonstrated ability to navigate ambiguous
architectures and data sources;
Azure cloud services;
Database (SQL) knowledge, database management, business intelligence, enterprise performance
management, data mining, and systems administration;
Expertise in middle tier/backend technologies such as .NET, relational and/or non-relational
(NoSQL) databases, web services, and RESTful concepts; and
Working knowledge of BI Governance, Data Governance, Master Data Management, and Data
Quality Management.
50% telecommuting permitted.
To apply, mail resume to: Christina Hetzer, ADM; PO BOX 1470, Decatur, IL 62525 (reference: KY0130)

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