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Multimodal Learning Jobs in California (NOW HIRING)

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

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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 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 as a Multimodal Learning Specialist, 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 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 June 2026, with employment types broken down into 7% Locum Tenens, 35% As Needed, 26% Full Time, 1% Part Time, 30% Temporary, and 1% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution.
Senior Machine Learning Engineer - Ads Signals Intelligence and Information Retrieval

Senior Machine Learning Engineer - Ads Signals Intelligence and Information Retrieval

Apple

Cupertino, CA • On-site

$151K - $199K/yr

Full-time

Posted 25 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

At Apple, we focus deeply on our customers' experience. Apple Ads brings this same approach to advertising, helping people find exactly what they're looking for and helping advertisers grow their businesses. ..Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands. Because when advertising is done right, it benefits everyone.
Apple's Ads Signals Intelligence team is seeking a hands-on and experienced Machine Learning Engineer to develop the next generation of ML-driven signal platforms that power retrieval, prediction, and relevance across Apple's advertising ecosystem-including the App Store and Apple News. This role focuses on building content understanding systems and large-scale infrastructure capable of delivering near real-time signal updates, enabling smarter, privacy-aware decision-making throughout the ad delivery stack. This role focuses on developing rich semantic signals from a variety of sources-including queries, creatives, metadata, and user interactions-to support scalable ad retrieval, creative ranking, and marketplace optimization. You'll work at the forefront of LLM fine-tuning, knowledge graph construction, semantic search, and multimodal representation learning to extract structured intelligence from unstructured data. While ad tech knowledge is a strong bonus, the core of the role is building high-quality, privacy-centric signals that fuel some of Apple's most advanced machine learning systems. As part of the Ads Signals Intelligence team, you'll be shaping the foundation of Apple's ad ranking and relevance systems through world-class signal understanding. You'll work on problems at the cutting edge of retrieval, multimodal learning, LLMs, and content intelligence-while contributing to Apple's mission to deliver high-performing, privacy-first advertising experiences at scale.
4+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understandingDeep understanding of information retrieval, semantic search, and query-document matchingStrong hands-on experience with LLM fine-tuning, knowledge graph construction, and entity-centric modelingExperience working with multimodal models, including text, vision, metadata, or audio-based representationsProficiency in Python, and experience with one or more of ML frameworks like PyTorch, TensorFlowBackground in statistical modeling, optimization, and ML theoryExposure to ad tech domains such as auction modeling, targeting, attribution, or creative optimization is a plusDemonstrated ability to deliver high-impact ML solutions in production environmentsBachelor's in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field.
7+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understandingMS or PhD in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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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