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Research Federated Learning Jobs (NOW HIRING)

Research Engineer, Privacy

San Francisco, CA ยท On-site

$380K - $445K/yr

... federated learning, and data memorization. Moreover, you will focus on investigating the ... Have hands-on research or production experience with PETs. * Are fluent in modern deep-learning ...

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose ... federated learning) that can be deployed at OpenAI scale. โ€ข Measure and strengthen model ...

The position involves conducting research in federated learning, and network optimization for 6G/FutureG wireless networks especially on the Internet of Intelligent Things under the supervision of Dr.

Academic Collaboration

Sydney, FL ยท On-site

$88.30K - $112.50K/yr

Active Researchers: Current faculty, postdocs, or PhD candidates actively publishing in relevant fields. * Relevant Expertise: Background in distributed ML, model parallelism, federated learning, or ...

Experience with privacy-preserving AI such as federated learning or secure execution * Strong mathematical foundation in ML optimization and model theory * Experience integrating novel research into ...

AIML Privacy-Engineering Rotation

Cupertino, CA ยท On-site

$124.60K - $159.20K/yr

Experience with differential privacy or private federated learning.BS in Computer Science, EE or ... Ability to learn and research new technologies and use-cases rapidly, assess privacy exposures, and ...

POSTDOCTORAL ASSOCIATE

New York, NY ยท On-site

$62.50K - $67.50K/yr

Conducting advanced research in machine learning and data analytics for power system operation and ... Federated learning * Multimodal machine learning * Large language models * Power system ...

The AI Research Scientist will contribute to webAI's development of next-generation AI models and ... Familiarity with privacy-preserving ML techniques such as federated learning * Experience ...

Advance privacy-preserving GenAI techniques including federated learning, differential privacy, and ... Publish research findings in top-tier AI/ML publications * Present at academic conferences ...

Advance privacy-preserving GenAI techniques including federated learning, differential privacy, and ... Publish research findings in top-tier AI/ML publications * Present at academic conferences ...

The AI Research Scientist will contribute to webAI's development of next-generation AI models and ... Familiarity with privacy-preserving ML techniques such as federated learning * Experience ...

Working within a highly collaborative, research-driven environment, you will guide ... Experience in federated learning, distributed training, or privacy-preserving ML is considered a ...

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Research Federated Learning information

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$2.4K

$5.3K

$7.7K

How much do research federated learning jobs pay per month?

As of May 29, 2026, the average monthly pay for research federated learning in the United States is $5,290.17, according to ZipRecruiter salary data. Most workers in this role earn between $3,000.00 and $7,500.00 per month, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Researcher in Federated Learning, and why are they important?

To thrive as a Researcher in Federated Learning, you need a strong background in machine learning, distributed systems, and statistics, typically supported by an advanced degree in computer science or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow Federated, and experience with privacy-preserving algorithms are essential. Critical thinking, collaboration, and effective communication are key soft skills for designing experiments and sharing findings with peers. These competencies are vital for advancing privacy-aware AI solutions and producing impactful research in this rapidly evolving domain.

What are some common challenges faced by professionals working in Research Federated Learning, and how can they be addressed?

Professionals in Research Federated Learning often encounter challenges such as ensuring data privacy across distributed devices, managing non-iid (non-independent and identically distributed) data, and optimizing communication efficiency between clients and servers. Addressing these issues requires strong collaboration with cross-functional teams, including data engineers, security experts, and software developers, to develop robust protocols and algorithms. Staying updated with the latest research and participating in open-source collaborations can also help overcome technical hurdles and drive innovation in this rapidly evolving field.

What is a Researcher in Federated Learning?

A Researcher in Federated Learning is a professional who studies, develops, and improves federated learning algorithms and systems. Federated learning is a machine learning approach where data remains decentralized, allowing multiple devices or organizations to collaboratively train models without sharing raw data. These researchers focus on advancing privacy, efficiency, and performance in distributed AI systems. Their work often involves experimenting with new methods, publishing findings, and contributing to the growing field of privacy-preserving machine learning.

What is the difference between Research Federated Learning vs Data Scientist?

AspectResearch Federated LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm developmentBusiness environments, analytics teams; focus on data analysis and insights
Industry UsageAI research, privacy-preserving ML, distributed systemsBusiness intelligence, marketing, finance, healthcare

Research Federated Learning involves developing privacy-focused, distributed machine learning algorithms, often in research or specialized tech settings. Data Scientists analyze data to generate insights and support decision-making in various industries. While both roles require strong analytical skills, Research Federated Learning emphasizes algorithm development and privacy, whereas Data Scientists focus on data analysis and reporting.

More about Research Federated Learning jobs
What cities are hiring for Research Federated Learning jobs? Cities with the most Research Federated Learning job openings:
What states have the most Research Federated Learning jobs? States with the most job openings for Research Federated Learning jobs include:
Infographic showing various Research Federated Learning job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 97% Full Time, and 2% Contract. Highlights an 98% Physical, and 2% Hybrid job distribution, with an average salary of $63,482 per year, or $30.5 per hour.

Machine Learning Research Scientist

Dynamis Labs

San Francisco, CA โ€ข On-site

$120K - $300K/yr

Full-time

Medical, Dental, Vision

Posted 15 days ago


Job description

Position Overview
Sentra is building organizational superintelligence through memory infrastructure that reasons across time, causality, and context. As a Research Scientist, you will tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression. You will design and implement systems that maintain execution state for entire organizations, consolidate millions of micro-events into durable knowledge, and learn patterns that predict events before it happens.
Key Responsibilities
  • Build LLM-powered information extraction pipelines that process unstructured communications and text data into structured entity-relationship representations.
  • Develop memory consolidation algorithms that validate information through multiple observations, merge duplicate entities, and prune ephemeral data.
  • Design temporal knowledge graph architectures that model organizational execution state as living, continuously updated systems rather than static records.
  • Create graph attention mechanisms and reasoning systems for complex causal queries about blockers, dependencies, and outcome patterns.
  • Research lossy semantic compression using information-theoretic principles to condense event streams into query-relevant long-term memory.
  • Design entity resolution systems handling identity evolution where entities merge, split, and transform through time.
  • Build meta-learning systems that identify organizational patterns and recognize when current situations match historical success or failure indicators.
  • Develop privacy-preserving cross-organizational learning using federated learning and differential privacy techniques.
  • Publish research findings and contribute to the broader research community on knowledge graphs and organizational intelligence.

Must-have Requirements
  • 5+ years building novel systems in machine learning, NLP, knowledge graphs, or related areas with evidence through publications, production implementations, or significant open-source contributions.
  • Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning demonstrated through shipped systems and architectural exploration.
  • Strong ML and NLP foundation, particularly in information extraction, entity resolution, or semantic representation.
  • Proficiency in Python and modern ML frameworks (PyTorch preferred) with experience deploying models at scale.
  • Track record of publishing research (conference papers, technical blog posts, or detailed technical documentation) and exploring novel architectures.
  • Ability to move between theoretical investigation and practical implementation, shipping research into production.

Bonus skills:
  • Graph databases (Neo4j, TigerGraph, Neptune) and query optimization for large-scale graphs.
  • Information theory, compression, or temporal data structures.
  • Causal inference, probabilistic reasoning, or Bayesian methods.
  • Distributed systems, stream processing, or real-time ML serving.
  • Human memory and cognition models.
  • Privacy-preserving ML (federated learning, differential privacy, secure multi-party computation).
  • Enterprise AI systems, workflow automation, or organizational software.
  • Publications at top-tier conferences (NeurIPS, ICML, ICLR, KDD, EMNLP, ACL, WWW, SOSP, OSDI).

Compensation and Benefits
  • Base Salary: $150,000 - $300,000
  • Equity: 0.3% - 2% depending on level
  • Comprehensive Health Coverage: Medical, dental, and vision
  • Wellness & Productivity Stipend: $2,500/month to cover meals, transport, gym memberships, or other personal productivity needs
  • Hardware & Tools: Latest MacBook Pro and AI development tools (ChatGPT Pro, Claude Pro, Cursor, etc.)
  • Learning & Growth: Dedicated budget for conferences, courses, and professional development
  • Relocation Support: Available for on-site hires
  • Flexible Time Off Policy

Total estimated annual benefits package: ~$30K-$35K in addition to base and equity.