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Research Machine Learning Federated Learning Jobs in California

... federated learning, and quantum machine learning). • Develops and publishes research findings in the form of presentations and conference papers. • Conducts research on machine learning and ...

... 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 ...

We have an opening for Machine Learning Research experts to join our team and advance the discipline as well as apply cutting edge tools and techniques to some of society's most important problems.

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 ...

Lead a team conducting research using machine learning methodologies that integrate financial theory with deep learning and reinforcement learning * Design and develop models that convert AI ...

MSCI is establishing a Machine Learning Center of Excellence within the Research & Development team to develop machine learning models that power investment tools for institutional clients. We seek ...

Lead a team conducting research using machine learning methodologies that integrate financial theory with deep learning and reinforcement learning * Design and develop models that convert AI ...

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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?

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:

Machine Learning Research Scientist

Dynamis Labs

San Francisco, CA • On-site

$120K - $300K/yr

Full-time

Medical, Dental, Vision

Re-posted 26 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.