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Research Machine Learning Federated Learning Jobs in California

Apple

Machine Learning Engineer

Sunnyvale, CA

$150K - $277K/yr

The Camera & Depth Architecture organization is responsible for research, design, and specifications of cameras and sensors for iPhone and other Apple products. As part of our machine learning team ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Research and development on cutting-edge machine learning technologies. Qualifications and Skills: * Graduate degree in Computer Science with a strong background in machine learning required.

Hive

Machine Learning Manager

San Francisco, CA

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine learning team. We are looking for an exceptional leader to help us with that growth, making sure that ...

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

We apply deep learning research to large scale EEG datasets collected on affordable hardware to ... About the Role We're seeking a talented Machine Learning Researcher to join our core R&D team. This ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Research and development on cutting-edge machine learning technologies. Qualifications and Skills: * Graduate degree in Computer Science with a strong background in machine learning required.

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine learning team. We are looking for an exceptional leader to help us with that growth, making sure that ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

You will work cross-functionally to bring innovative machine learning solutions from research and experimentation through to robust, production-grade deployment. Description The MLE will collaborate ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Familiarity with CNNs, RNN, LSTMs, and the latest research trends. * Experience implementing, deploying, and maintaining production machine learning systems. * Experience monitoring and optimizing ...

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Research Machine Learning Federated Learning information

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 are popular job titles related to Research Machine Learning Federated Learning jobs in California? For Research Machine Learning Federated Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in California look for? The top searched job categories for Research Machine Learning Federated Learning jobs in California are:
What cities in California are hiring for Research Machine Learning Federated Learning jobs? Cities in California with the most Research Machine Learning Federated Learning job openings:
Research Machine Learning Federated Learning jobs near you
Machine Learning Engineer

Machine Learning Engineer

MM International

Fremont, CA • On-site

Contractor

Re-posted 17 days ago

Job description

Role: Machine Learning Engineer

Location: Fremont, CA 

 

once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a general video screening with PV. Then we send the submission to the client

About the Role:

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations. You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.

You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations. Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise. You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities

  • Design, develop, and deploy machine learning models for factory and warehouse environments.
  • Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.
  • Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.
  • Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
  • Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.
  • Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.
  • Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

Minimum Requirements

  • In-depth knowledge of Python for high-performance, data-intensive applications.
  • Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).
  • Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.
  • Foundational knowledge of statistics for model comparison and performance assessment.
  • Real-world experience deploying and maintaining machine learning solutions in production environments.
  • Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

Preferred Qualifications

  • CI/CD, Kubernetes, MLflow, TensorFlow, PyTorch, AWS.
  • Experience working in manufacturing, industrial automation, or warehouse environments.
  • Familiarity with multi-modal data integration and analysis.
  • Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.
  • Excellent communication skills for cross-functional teamwork.