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Research Machine Learning Federated Learning Jobs in Mountain View, CA

Stackruit Ltd.

Machine Learning

San Francisco, CA · On-site

$200 - $250/hr

First Machine Learning Engineer (US Remote - $200k-$250k) Do you dream of using machine learning to empower businesses to take action on their data? Join a mission-driven company! About Us * We're ...

OpenAI

Research Engineer, Privacy

San Francisco, CA · On-site

$380K - $445K/yr

... machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning ... Have hands-on research or production experience with PETs. * Are fluent in modern deep-learning ...

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

We apply deep learning research to large scale neural datasets to decode internal thought directly ... You will design and implement advanced machine learning models for EEG-based neural decoding ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Mountain View, CA

Research Machine Learning Federated Learning information

See Mountain View, CA salary details

$30.1K

$50.2K

$103.8K

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

As of Aug 29, 2026, the average yearly pay for research machine learning federated learning in Mountain View, CA is $50,235.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,300.00 and $54,300.00 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 job categories do people searching Research Machine Learning Federated Learning jobs in Mountain View, CA look for?

The top searched job categories for Research Machine Learning Federated Learning jobs in Mountain View, CA are:

What cities near Mountain View, CA are hiring for Research Machine Learning Federated Learning jobs?

Cities near Mountain View, CA 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 Mountain View, CA 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 $50,235 per year, or $24.2 per hour.

Azure Machine Learning Engineer

InterSources Inc

Santa Clara, CA • On-site

$64.50 - $80.25/hr

Full-time

Re-posted 5 days ago

Job description

Job Summary:
InterSources Inc is a Certified Diverse Supplier and Award-Winning Global Software Consultancy that offers innovative solutions for Digital Transformations. The Azure Machine Learning Engineer will design and implement scalable Azure cloud solutions, focusing on AI services and data analytics.
Responsibilities:
• Prior Experience Hands on experience on working in Azure cloud platform.
• Experience in designing and implementing scalable and secure Azure cloud solutions using Data Bricks Client platform.
• Understanding of implementation of architecture within Azure Big Data Analytics Al tools.
• Expert level in Designing and Architect solutions in Azure Data factory, Azure Databricks, Azure Datalake, Delta Lake and Azure synapse analytics implementation.
• Experience in Azure cloud technologies like PySpark, Synapse, ADF, Databricks, Python, Scala and SQL.
• Have good experience configuring Microservices using Docker, Kubernetes on Azure Data Bricks
• Extensive Experience on working on Azure AI services including Data Bricks and Azure cognitive services.
• Highly preferred experience on working with Azure OpenAI services.
• Hands-on experience on design, and optimizing LLM, natural language processing (NLP) systems, frameworks, and tools.
• Hands-on AI/Client modeling experience of complex datasets combined with a strong understanding of the theoretical foundations of AI/Client.
• Expertise in most of the following areas: supervised & unsupervised learning, deep learning, reinforcement learning, federated learning, time series forecasting, Bayesian statistics, and optimization.
• Experience in creating and deploying code libraries using functions and classes in Python in AI product-focused development.
• Well-versed in software development and code quality best industry practices.
• Experience working with Agile Development methodologies.
Qualifications:
Required:
• Prior Experience Hands on experience on working in Azure cloud platform.
• Experience in designing and implementing scalable and secure Azure cloud solutions using Data Bricks Client platform.
• Understanding of implementation of architecture within Azure Big Data Analytics Al tools.
• Expert level in Designing and Architect solutions in Azure Data factory, Azure Databricks, Azure Datalake, Delta Lake and Azure synapse analytics implementation.
• Experience in Azure cloud technologies like PySpark, Synapse, ADF, Databricks, Python, Scala and SQL.
• Have good experience configuring Microservices using Docker, Kubernetes on Azure Data Bricks.
• Extensive Experience on working on Azure AI services including Data Bricks and Azure cognitive services.
• Hands-on experience on design, and optimizing LLM, natural language processing (NLP) systems, frameworks, and tools.
• Hands-on AI/Client modeling experience of complex datasets combined with a strong understanding of the theoretical foundations of AI/Client.
• Expertise in most of the following areas: supervised & unsupervised learning, deep learning, reinforcement learning, federated learning, time series forecasting, Bayesian statistics, and optimization.
• Experience in creating and deploying code libraries using functions and classes in Python in AI product-focused development.
• Well-versed in software development and code quality best industry practices.
• Experience working with Agile Development methodologies.
Preferred:
• Highly preferred experience on working with Azure OpenAI services.
Company:
InterSources Inc. solves operational problems where protection, performance, compliance, AI, and workforce capability must work together. Founded in 2007, the company is headquartered in Fremont, USA, with a team of 501-1000 employees. The company is currently Late Stage.
InterSources logo

About InterSources

Sourced by ZipRecruiter

In 2007, Our journey began as pioneers in the realm of technology and security. Since then, InterSources Inc. has evolved into a trusted partner, leading the way in Cloud Security, Cybersecurity, PLG Consulting, Digital Transformation, and Professional Services. With a rich history of excellence and a forward-thinking approach, we continue to secure your digital future and drive innovation. Explore our legacy of success and discover the possibilities that lie ahead.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

Fremont, CA, US

Social media

Twitter

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