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Research Machine Learning Federated Learning Jobs in Hartford, CT

Machine Learning Engineer

Hartford, CT · On-site

$62K - $100K/yr

Your Journey at Crowe Starts Here: At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you're trusted to deliver results and make an ...

Senior AI Machine Learning Engineer

Hartford, CT · On-site

$123K - $162K/yr

The Hartfordis seeking aSenior AI Machine Learning Engineerwithin Employee Benefits Applied AI and Analytics (EB AIA) to help build, deploy, and sustain enterprise-scale predictive and applied AI ...

Whether lasers, machine tools, EUV or electronics - TRUMPF is building technological worlds for ... learning. Internship Duatlon: Onsite in Farmington, CT from January 2027 to August 2027 for at ...

Whether lasers, machine tools, EUV or electronics - TRUMPF is building technological worlds for ... learning. Internship Duatlon: Onsite in Farmington, CT from January 2027 to August 2027 for at ...

Whether lasers, machine tools, EUV or electronics - TRUMPF is building technological worlds for ... learning. Internship Duatlon: Onsite in Farmington, CT from January 2027 to August 2027 for at ...

Whether lasers, machine tools, EUV or electronics - TRUMPF is building technological worlds for ... learning. Internship Duatlon: Onsite in Farmington, CT from January 2027 to August 2027 for at ...

Showing results 21-40

Research Machine Learning Federated Learning information

See Hartford, CT salary details

$25.7K

$43K

$88.8K

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

As of Sep 7, 2026, the average yearly pay for research machine learning federated learning in Hartford, CT is $42,955.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,800.00 and $46,400.00 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 Hartford, CT?

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

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

The top searched job categories for Research Machine Learning Federated Learning jobs in Hartford, CT are:

Machine learning Solution Architect: Remote Role

Qualis1 Inc

Hartford, CT • Remote

$64.50 - $85/hr

Contractor

Re-posted 25 days ago


Job description

Role: AI/ Machine learning Architect

Location: Hartford, CT, remote

Duration: 6+ months

Description:

Need Machine learning Architect with Azure cloud