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Research Machine Learning Federated Learning Jobs in California

Jobs for Humanity

Machine Learning Engineers

San Jose, CA · On-site

$194K - $355K/yr

Company Name: Tiktok Senior Research Engineer, Machine Learning Privacy San Jose Regular R D ... with federated learning / distributed machine learning algorithms and experienced in federated ...

Omni Inclusive

Data Scientist

Santa Clara, CA · On-site

... of AI/Client (Research Oriented). Expertise in most of the following areas: supervised unsupervised learning, deep learning, reinforcement learning, federated learning, time series forecasting ...

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 ...

OpenAI

Research Engineer, Privacy

San Francisco, CA · On-site

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose ... machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning ...

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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 Engineers

Jobs for Humanity

San Jose, CA • On-site

$194K - $355K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

Machine Learning Engineers

Jobs for Humanity is collaborating with Upwardly Global and with Tiktok to build an inclusive and just employment ecosystem. We support individuals coming from all walks of life. Company Name: Tiktok

Job Description

Senior Research Engineer, Machine Learning Privacy San Jose Regular R D Machine learning Job ID: M1758 Responsibilities TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. Why Join Us Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible. Together, we inspire creativity and bring joya mission we all believe in and aim towards achieving every day. To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At TikTok, we create together and grow together. That's how we drive impactfor ourselves, our company, and the communities we serve. Join us. At TikTok, we treat privacy as our first priority in our product design and implementation. Privacy is not just about regulation compliance, but also about a more trusted way to enable technology innovation by respecting users' privacy choices! Privacy Innovation (PI) Lab is established to explore the next frontier of privacy technology and theory in the digitalized world. We provide key insights and technical solutions on privacy-related innovation for all TikTok's products. Furthermore, we also collaborate with worldwide technical and academic communities to build an open ecosystem to promote privacy friendly digital experience. PI Lab is growing fast and seeking highly experienced, bright, and capable researchers and professionals to join our team. As a member of PI Lab, you will have the opportunities to research advanced privacy technology and theory together with worldwide influential researchers, and to tackle the critical industrial challenges on privacy innovation serving billions of TikTok users by applying cutting-edge technology. Responsibilities - Participate in the construction of a multi-party joint modeling and data analysis platform - Work with product teams to understand key privacy requirements from TikTok product family and convert research outcomes into technical solutions and product prototypes - Work with research teams for complex experiments requiring optimized algorithms running on large datasets or sophisticated data processing - Build open-source tools and infrastructure for privacy related research and engage with community contributors in external events including meetups, hackathons, summits Qualifications Minimum Requirements: - PhD or Master's degree in Computer Science or a related field, or 3+ years of experience working in related fields - Familiar with federated learning / distributed machine learning algorithms and experienced in federated learning frameworks and applications development - Familiar with machine learning and deep learning frameworks, like TensorFlow, Pytorch, JAX - Interest in privacy-enhanced techniques (PET) and related technical domains, including data and identity anonymization, differential privacy, secure multi-party computation, federated machine learning, on-device machine learning, interpretable AI, privacy-preserving technology for large language models (LLM) or foundation models, privacy-preserving regulation technology, etc. - Proficiency in at least one of the following programming languages, Go, C++, Python, Rust, Java - Ability to work globally and collaboratively within a team

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too. TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://shorturl.at/cdpT2

Job Information For Pay Transparency Compensation Description (annually) The base salary range for this position in the selected city is $194,000 - $355,000 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Our company benefits are designed to convey company culture and values, to create an efficient and inspiring work environment, and to support our employees to give their best in both work and life. We offer the following benefits to eligible employees: - We cover 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents and offer a Health Savings Account(HSA) with a company match - Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans - Flexible Spending Account(FSA) Options like Health Care, Limited Purpose and Dependent Care - 10 paid holidays per year plus 17 days of Paid Personal Time Off (PPTO) (prorated upon hire and increased by tenure) and 10 paid sick days per year as well as 12 weeks of paid Parental leave and 8 weeks of paid Supplemental Disability - Mental and emotional health benefits through our EAP and Lyra - 401K company match, gym and cellphone service reimbursements The Company reserves the right to modify or change these benefits programs at any time, with or without notice. For Los Angeles County (unincorporated) Candidates: Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues 2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems 3. Exercising sound judgment

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