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Multimodal Learning Jobs in California (NOW HIRING)

KenkoTech Futures

AI Scientist

Fremont, CA · On-site

Develop methods for multimodal learning across diverse biological datasets * Improve model performance through post-training, evaluation, alignment and fine-tuning techniques * Work closely with ...

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Helix AI Engineer, Modeling

San Jose, CA · On-site

Required : • Experience designing and training deep learning models for vision, language, or multimodal systems • Strong understanding of modern model architectures (e.g., transformers and ...

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Helix AI Engineer, Modeling

San Jose, CA · On-site

$200K - $400K/yr

Advance multimodal learning approaches, including fusion, alignment, and cross-modal reasoning * Improve model capabilities in areas such as generalization, robustness, and long-horizon reasoning

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Multimodal Learning information

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are popular job titles related to Multimodal Learning jobs in California?

For Multimodal Learning jobs in California, the most frequently searched job titles are:

What job categories do people searching Multimodal Learning jobs in California look for?

The top searched job categories for Multimodal Learning jobs in California are:

What cities in California are hiring for Multimodal Learning jobs?

Cities in California with the most Multimodal Learning job openings:

Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI Scientist

KenkoTech Futures

Fremont, CA • On-site

Other

Posted 11 days ago

Job description

📍 Location: San Francisco / Boston

🧬 About: Frontier AI x Biology | Foundation Models | Therapeutic Discovery

💼 Stage: Well-funded AI Biotechnology Company


PLEASE FOLLOW THE KENKOTECH PAGE AND CONNECT WITH THE JOB POSTER


About the Opportunity


We're partnering with one of the world's leading AI x Biology companies, building frontier foundation models designed to transform therapeutic discovery.


The team is developing large-scale multimodal foundation models across biological data modalities - including single-cell genomics, transcriptomics, DNA, RNA and proteins - to create universal biological representations capable of accelerating target discovery, disease understanding and drug development.


This is a rare opportunity to join an exceptionally strong research organisation working at the intersection of large-scale machine learning and modern biology. You'll collaborate with world-class AI researchers, computational biologists and experimental scientists to develop the next generation of biological foundation models that directly power therapeutic discovery.


The role is highly research-focused, but with a strong emphasis on building models that move beyond publications and have real scientific impact.


Key Responsibilities

  • Develop and train large-scale foundation models across biological modalities including DNA, RNA, proteins and single-cell data
  • Research novel model architectures, representation learning approaches and pre-training strategies for biological data
  • Design and implement large-scale distributed training pipelines for frontier AI models
  • Develop methods for multimodal learning across diverse biological datasets
  • Improve model performance through post-training, evaluation, alignment and fine-tuning techniques
  • Work closely with experimental scientists to translate model outputs into biological insight
  • Design rigorous evaluation frameworks for biological foundation models
  • Contribute to the long-term research direction of the company's AI platform
  • Stay at the forefront of developments across machine learning, foundation models and computational biology


Qualifications

  • PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics, Physics or a related quantitative discipline
  • Outstanding research background in modern machine learning
  • Strong publication record at leading conferences or journals (NeurIPS, ICML, ICLR, Nature, Science, Cell, etc.)
  • Experience developing, training or evaluating large deep learning models
  • Strong programming skills using modern ML frameworks (PyTorch, JAX, etc.)
  • Experience with distributed training or large-scale model development is highly desirable
  • Ability to work across both research and engineering to build production-quality systems
  • Ideal Background


We're particularly interested in researchers with experience in one or more of the following:

  • Foundation Models
  • Representation Learning
  • Large Language Models
  • Multimodal Learning
  • Self-Supervised Learning
  • Generative Modelling
  • Reinforcement Learning
  • Large-Scale Distributed Training
  • Single-Cell Foundation Models


Why Join

  1. Help build some of the world's most advanced foundation models for biology
  2. Work alongside internationally recognised AI researchers and computational biologists
  3. Apply frontier AI to real therapeutic discovery problems
  4. Access enormous proprietary biological datasets and large-scale compute
  5. Research with genuine scientific and clinical impact rather than purely academic objectives
  6. Join one of the best-capitalised and fastest-growing AI x Biology organisations in the world
  7. Opportunity to publish, innovate and help shape the future of AI-driven drug discovery

Apply