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Research Machine Learning Federated Learning Jobs in Jackson, MS

Vergent LMS

Software Support Analyst

Ridgeland, MS · On-site

... AI and machine learning capabilities. With talented employees nationwide and offices in Ridgeland, Mississippi and Plano, Texas, Vergent LMS is one of America's fastest-growing privately held ...

V2X

Aviation Machinist II - Madison, MS

Madison, MS

$17.75 - $24.25/hr

Ability to skillfully operate typical machine shop equipment as well as hand and power tools ... Learning and Development resources * Employee assistance resources Pay and benefits are subject to ...

Vergent LMS

Software Support Analyst

Ridgeland, MS

... AI and machine learning capabilities. With talented employees nationwide and offices in Ridgeland, Mississippi and Plano, Texas, Vergent LMS is one of America's fastest-growing privately held ...

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Showing results 21-40

All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Jackson, MS

Research Machine Learning Federated Learning information

See Jackson, MS salary details

$22.2K

$37.1K

$76.7K

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

As of Jul 25, 2026, the average yearly pay for research machine learning federated learning in Jackson, MS is $37,108.00, according to ZipRecruiter salary data. Most workers in this role earn between $28,300.00 and $40,100.00 per year, depending on experience, location, and employer.

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

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 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 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 job categories do people searching Research Machine Learning Federated Learning jobs in Jackson, MS look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Jackson, MS are:
What cities near Jackson, MS are hiring for Research Machine Learning Federated Learning jobs? Cities near Jackson, MS with the most Research Machine Learning Federated Learning job openings:
Research Machine Learning Federated Learning jobs near you
Infographic showing various Research Machine Learning Federated Learning job openings in Jackson, MS as of June 2026, with employment types broken down into 25% As Needed, 50% Part Time, and 25% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $37,108 per year, or $17.8 per hour.
Data Scientist/Developer

Data Scientist/Developer

Accord Technologies Inc.

Jackson, MS • On-site

Contractor

Posted 4 days ago

Job description

Data scientist/developer
Jackson, MS (Remote)
5 months Contract
 
 
Job Requirement:

Data scientist/software developer to support a proof of- concept demonstration using natural language processing and other machine learning methods to improve the intake process.
This work is critical to demonstrating the potential of the latest technology to improve the lives of children at risk.

The Data Scientist/Developer will be responsible for supporting the development, implementation, and testing of

statistical models, integration of NLP, and refinement and testing of the prototype. The data scientist will work closely

with State stakeholders and technical team members to ensure the quality of the results and that the derived methods

are transparent, statistically sound, relevant, and documented.

Key Responsibilities

• Current Processes & Technology

o Collectively engage with MDCPS and other team members to understand the current intake process and

outcomes.

o Identify how the State decides to deploy resources based on the intake information.

o Contribute to the identification of shortcomings in the intake process and opportunities to improve outcomes.

Use information from interviews, discovery sessions, and workshops to identify.

o Identify any internal data sources used in the intake process.

• Devise New Intake Approach Using New Technologies

o Based on an understanding of the current intake process and its shortcomings, devise and propose an

improved process using natural language processing and other machine learning methods to favorably impact child

outcomes while reducing resources.

o Quantify to the extent possible, the impact of the improved process and use of new technology.

• Map Anticipated Data Source Changes

o Determine how internal data sources might change with future modifications to core IT systems used by

MDCPS.

o Adjust the proposed intake process to account for any data source changes

• Design Review(s)

o Conduct a preliminary and a final design review of an improved intake tool proof-of-concept implementation.

o Include anticipated outcomes from the use of the technology and any differences that may be evident from the

proof-of-concept implementation.

o If an LLM is intended to be used, show how the data will be protected.

o Identify the source of the data that will be used in the proof-of-concept implementation. If data from the State is

unavailable, describe an alternative approach.

• Implementation of Proof-of-Concept

o Create a means of hosting data, whether the data is provided by the State, simulated, or other means.

o Construct a demonstrable prototype application that will illustrate the new technology’s impact on children and

State resources.

o Build the prototype application using Python, C++, JAVA, and/or SQL, or similar language. Use Postgres or a

similar database if needed.

o Integrate the proof-of-concept with the available data source.

o Conduct tests to validate the functionality of the application.

o Validate to the extent possible, the impact on children and State resources from using the prototype in a fully

implemented form.

o Seek validation of the application’s efficacy from key State stakeholders through one-on-one demonstrations.

• Conference Room Demonstration

o During 3-4 days, provide a conference room demonstration that shows how the prototype application can

improve child outcomes and reduce State resources.

o Provide stakeholders a hands-on-experience with the application.

• Agile Development Process

o Participate in the Agile development process to ensure the success of the project.

Requirement Details:

• Bachelor’s or Master’s degree in computer science, engineering, physics, or related field.

• Have participated in US Federal Gov’t data science programs requiring TS/SCI clearance, delivering solutions

requiring the combination of geospatial disciplines, and pattern of life analysis.

• Proven expertise custom developing AI programs “from the ground up”, including but not limited to, text

processing, and optimized selection and application of multiple LLMs.

• Minimum two (2) years of experience designing and implementing machine-learning solutions based on first

principles, including developing custom statistical methods without reliance on pre-built libraries.

• Minimum academic math background to include full calculus series, linear algebra, and statistics. Discrete

math, advanced statistics, and differential equations are a plus.

• Knowledge and competence in databases such as Postgres, MySQL, SQL Server, as well as Python, C++,

JAVA, React, NextJS, NodeJS, and AWS.

• Experience deploying analytic models in pilot or AWS production environments.

• Good communication skills with both technical and non-technical people.

• Strong understanding of model validation and performance measurement.

• Experience deploying advanced analytic solutions in public-sector or regulated environments.