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Multimodal Learning Jobs in California (NOW HIRING)

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

... multimodal learning). • Ability to work effectively in an early-stage environment where scope is broad and priorities shift fast. Preferred : • Prior work at a frontier AI lab or data company ...

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

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

Showing results 21-40

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 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 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 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:
Infographic showing various Multimodal Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Applied ML Researcher - Video Apps

Apple

Cupertino, CA • On-site

$185K/yr

Full-time

Re-posted 7 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

We are seeking a Senior Applied ML Researcher to design, train, and deploy state-of-the-art models for visual and audio understanding. You will work on challenging problems at the intersection of computer vision, audio signal processing, and multimodal learning, enabling intelligent systems that can see, hear, and reason about the world.
You will collaborate closely with research scientists, engineers, and product teams to find novel applications of Deep Machine Learning capabilities to assist our creative user base. Your mission is to elevate the workflows of millions of creators by combining generative AI with Apple's human-centered design principles.
Description
Design and train deep neural networks for video, image, audio, and audio-visual tasks.
Build models for audio-visual representation learning, cross-modal alignment, and fusion.
Develop solutions for tasks such as:
Video understanding and temporal modeling.
Audio-visual event detection.
Speech, sound, and scene understanding.
Multimodal classification, detection, and localization.
Minimum Qualifications
4+ years of experience in deep learning or machine learning engineering
strong expertise in deep neural networks and modern training workflows
8 years + Hands-on experience with computer vision and/or audio modeling
Proficiency in Python and deep learning frameworks (PyTorch preferred)
Solid understanding of linear algebra, probability, and optimization
Ability to build intuition from problem statement and translate to dataset requirement, neural network design and loss functions
Preferred Qualifications
PhD in computer science, machine learning, or a related field, or equivalent practical experience.
Publications in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, etc.)
Experience with self-supervised or foundation model pre-training
Open-source contributions in vision, audio, or multimodal AI
Bonus: Experience with Objective-C and/or Swift for on-device deployment

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976