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Research Machine Learning Federated Learning Jobs in Baltimore, MD

Bespoke Labs

Machine Learning Engineer

Annapolis, MD · On-site

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Towson, MD

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Annapolis, MD

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Towson, MD · On-site

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Bowie, MD · On-site

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Largo, MD · On-site

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Baltimore, MD · On-site

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Largo, MD

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

Bespoke Labs

Machine Learning Engineer

Baltimore, MD

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Baltimore, MD

Research Machine Learning Federated Learning information

See Baltimore, MD salary details

$25.3K

$42.3K

$87.4K

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

As of Sep 6, 2026, the average yearly pay for research machine learning federated learning in Baltimore, MD is $42,313.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,300.00 and $45,700.00 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 Baltimore, MD?

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

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

The top searched job categories for Research Machine Learning Federated Learning jobs in Baltimore, MD are:

What cities near Baltimore, MD are hiring for Research Machine Learning Federated Learning jobs?

Cities near Baltimore, MD 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 Baltimore, MD 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 $42,313 per year, or $20.3 per hour.

Machine Learning Engineer II, Logistics AI

Instacart

Bowie, MD

Full-time

Re-posted 17 days ago

Instacart rating

6.9

Company rating: 6.9 out of 10

Based on 33 frontline employees who took The Breakroom Quiz

35th of 64 rated delivery companies

Job description

We're transforming the grocery industry

At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.

Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.

Instacart is a Flex First team

There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.

About the Role:

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's marketplace systems. You will use machine learning to devise and refine solutions in crucial areas such as routing optimization, pricing, dispatch, and mapping. You will actively contribute to initiatives, assisting in all stages from the initial concept, through prototyping and experimentation, to final implementation.

About the Team:

The Logistics AI group is responsible for the intelligence and execution behind Instacart’s fulfillment system. The team optimizes a multi-sided marketplace to ensure customers get their orders on-time and in high quality, shoppers get efficient and fulfilling work, and retailers and consumer brands get reasonable business. The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial, as well as foundational problems executing on a high throughput system with dynamic data.

About the Job:

  • Design, develop, and deploy machine learning solutions to tackle practical challenges in the marketplace.
  • Collaborate closely with product managers, data scientists, and backend engineers to deeply understand business needs and create impactful ML/AI applications.
  • Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals.
  • Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.

About You:

Minimum Qualifications:

  • Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience 
  • Have strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and Machine Learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
  • Have strong analytical skills and problem-solving ability
  • Are a strong communicator who can collaborate with diverse stakeholders across all levels

Preferred Qualifications:

  • Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets
  • Knowledge of deep learning frameworks and methodologies
  • Experience in applying machine learning and optimization techniques to solve marketplace problems

Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.

Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.

For US based candidates, the base pay ranges for a successful candidate are listed below.

What Instacart employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Instacart logo

About Instacart

Sourced by ZipRecruiter

Instacart, based in San Francisco, CA, US, operates within the retail industry, specifically grocery delivery and pick-up service. It is recognized as a pioneer in this field, delivering fresh groceries from local stores directly to customers' doors. The company, which launched its services in 2012, continues to pioneer change in the online grocery shopping sector through its commitment to cutting-edge technology, new business ideas, and dedicated service.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Francisco, CA, US

Year founded

2012

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