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Deep Learning Research Intern Jobs in San Rafael, CA

Hum AI

AI Researcher

San Francisco, CA · On-site

Deep understanding of current machine learning research. * Proven track record of generating new ideas or enhancing existing ones in machine learning, evidenced by first-author publications or ...

Canva

PhD Research Scientist Intern

San Francisco, CA

$157K/yr

... learning from and working alongside the researchers and engineerings creating Canva's next ... The space is designed for both deep work and collaboration, along with spaces for Canvanauts to ...

New

Canva

PhD Research Scientist Intern

San Francisco, CA · On-site

$157K/yr

... learning from and working alongside the researchers and engineerings creating Canva's next ... The space is designed for both deep work and collaboration, along with spaces for Canvanauts to ...

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How much do deep learning research intern jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for deep learning research intern in San Rafael, CA is $18.99, according to ZipRecruiter salary data. Most workers in this role earn between $16.06 and $21.44 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a deep learning research intern?

To thrive as a Deep Learning Research Intern, you need a strong grasp of machine learning fundamentals, programming skills (especially in Python), and a background in mathematics or computer science, often supported by relevant coursework or research experience. Familiarity with deep learning frameworks such as TensorFlow or PyTorch and proficiency in using data processing and visualization tools are typically expected. Creativity, problem-solving abilities, and effective communication help interns contribute novel ideas and collaborate within research teams. These skills are vital for developing, testing, and presenting cutting-edge AI models and solutions in a fast-evolving field.

What does a deep learning research intern do?

A Deep Learning Research Intern assists in the development and experimentation of machine learning models, particularly neural networks, to solve complex problems in fields like computer vision, natural language processing, or robotics. They typically work under the guidance of experienced researchers, contributing to tasks such as data preprocessing, model training, and result analysis. Interns may also help implement and optimize algorithms, read and summarize research papers, and prepare reports or presentations on their findings. This role provides hands-on experience with state-of-the-art AI technologies and research methodologies.

What are some common challenges faced by deep learning research interns, and how can they overcome them?

Deep Learning Research Interns often encounter challenges such as managing complex datasets, tuning neural network architectures, and keeping up with the latest research developments. It's common to spend significant time troubleshooting code or optimizing models for better performance. Collaborating closely with mentors and team members, actively participating in research discussions, and consistently reading recent papers can help interns overcome these hurdles. Utilizing open-source tools and frameworks, along with clear documentation practices, also streamlines the research process.
What cities near San Rafael, CA are hiring for Deep Learning Research Intern jobs? Cities near San Rafael, CA with the most Deep Learning Research Intern job openings:
Deep Learning Research Intern jobs near you

Machine Learning Research Scientist

Dynamis Labs

San Francisco, CA • On-site

$120K - $300K/yr

Full-time

Medical, Dental, Vision

Re-posted 27 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.

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