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Research Machine Learning Federated Learning Jobs

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

Wehave an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

Apple

AIML Privacy - Engineering Rotation

Cupertino, CA · On-site

$124K - $159K/yr

The Privacy Preserving Machine Learning team works with teams all across the company to provide ... Experience with differential privacy or private federated learning. BS in Computer Science, EE or ...

Optiver

Machine Learning Research Engineer

New York, NY · On-site

$224K/yr

Optiver is a seeking a Machine Learning Research Engineer to join our team, focusing on a pivotal AI initiative. This role would offer the opportunity to have significant impact across Machine ...

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All JobsResearch Machine Learning Federated Learning Jobs

Research Machine Learning Federated Learning information

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$25.5K

$42.6K

$88K

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

As of Jul 22, 2026, the average yearly pay for research machine learning federated learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 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.

More about Research Machine Learning Federated Learning jobs
What cities are hiring for Research Machine Learning Federated Learning jobs? Cities with the most Research Machine Learning Federated Learning job openings:
What states have the most Research Machine Learning Federated Learning jobs? States with the most job openings for Research Machine Learning Federated Learning jobs include:
What job categories do people searching Research Machine Learning Federated Learning jobs look for? The top searched job categories for Research Machine Learning Federated Learning jobs are:
Research Machine Learning Federated Learning jobs near you
Infographic showing various Research Machine Learning Federated Learning job openings in the United States as of July 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Senior Machine Learning Scientist

Senior Machine Learning Scientist

Teladoc Health, Inc.

Remote

Full-time

Posted 15 days ago

Job description

Join the team leading the next evolution of virtual care.
At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives.
Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we're transforming how better health happens.
Summary of Position
The Machine Learning effort is part of the Data Science team at Teladoc Health. In this role, you will partner with Product, Engineering, Clinical, Operations, Marketing and Data Engineering to design, build, deploy, and operate scalable machine learning and AI systems that power business-critical decision making. You will own the end-to-end machine learning lifecycle: from data and feature engineering through deployment, monitoring, experimentation, and continuous improvement (across both batch and real-time production environments). Your efforts and contributions will have a big impact on improving member and provider experience on the Teladoc Health platform.
This is an opportunity to apply technical rigor, scalable data processing tools, and machine learning algorithms to solve real-world business problems while engineering, deploying, measuring, and iterating machine learning solutions in production.
Essential Duties and Responsibilities
  • Build production ready time series models to predict real time KPIs as well as build optimal decision actions to manage the provider network for clinical operations business optimization
  • Propose, evaluate and interpret the results of your work for clinical, product and business decision-makers and own outcomes
  • Collaborate closely with peers and stakeholders to discover and distill requirements of problem definitions, product features and architecture to improve clinical outcomes using insights and models
  • Develop modular, well-tested, production-quality software using Python, Spark and SQL to build scalable data engineering, feature engineering, machine learning and AI pipelines following software engineering best practices.
  • Design, develop, deploy and operate scalable production machine learning and AI systems, including data transformation pipelines, feature pipelines, model training, evaluation, deployment, monitoring, retraining, and experiment tracking. Ensure robust model lifecycle management through model versioning, MLflow, automated testing, CI/CD, and production monitoring.
  • Build and optimize scalable Spark and Databricks workloads, leveraging distributed computing best practices for large-scale data processing and real-time inference.
  • Design, evaluate and integrate Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and other AI capabilities where appropriate to solve business problems.
  • Monitor production models and data pipelines for data quality, feature drift, concept drift, latency, reliability, and business performance, proactively identifying and resolving issues.

Qualifications Expected for Position
  • 8+ years of experience as a Machine Learning Scientist, Data Scientist or in a similar role within SaaS or consumer technology companies.
  • A Master's degree or higher in computer science, operations research, machine learning, information systems, engineering, or a related field
  • Demonstrated depth of experience developing clean, robust, and reusable production-quality code using Python, Spark, and SQL.
  • Extensive experience designing, building and operating production machine learning systems, including scalable software, distributed data processing, reusable feature engineering pipelines, model deployment, monitoring and continuous improvement.
  • Strong understanding of statistical modeling, machine learning algorithms, experimentation, model evaluation, forecasting, and explainability techniques, with the ability to select appropriate approaches based on business and technical constraints.
  • Excellent data analysis skills and bias to deliver, measure and iterate using experimentation and statistical analysis
  • Strong system design skills with the ability to architect scalable, maintainable, and observable machine learning solutions.
  • Ability to translate machine learning solutions into measurable business outcomes and effectively communicate technical decisions, tradeoffs, and expected value to both technical and business stakeholders.

Bonus Qualifications
  • Hands-on experience with modern data and ML platforms such as Databricks, MLflow, Delta Lake, Airflow, Terraform, or equivalent cloud-native technologies.
  • Experience building AI-powered applications using Large Language Models (LLMs), embeddings, vector databases, retrieval-augmented generation (RAG), agentic workflows, or equivalent AI technologies is highly desirable.
  • Experience applying machine learning, forecasting, optimization, or decision science techniques to large-scale operational, logistics, marketplace, or network optimization problems.
  • Experience working with healthcare data (e.g., claims or EHR) is a plus.
  • Great active listening skills to infer product/business needs and underlying context.
  • Ability to collaborate effectively with peers, and respect for member privacy.

The base salary range for this position is $150,000 - $175,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026. Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications. This information is applicable for all full-time positions.
#LI-SS2 #LI-Remote
We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details.
As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified.
Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.
Why join Teladoc Health?
  • Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission.
  • Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference.
  • Join a multi-faceted community that celebrates each colleague's unique perspective and is focused on continually improving, each and every day.
  • Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways.
  • Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs.
  • Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring #TeamTeladocHealth on LinkedIn.

As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind.
Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health's Notice of Privacy Practices for U.S. Employees' Personal information is available at this link.

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