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Multimodal Learning Jobs in Boston, MA (NOW HIRING)

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$124K - $149K/yr

Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain ...

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All JobsMultimodal Learning JobsMultimodal Learning Jobs in Boston, MA

Multimodal Learning information

See Boston, MA salary details

$22.8K

$67K

$124.4K

How much do multimodal learning jobs pay per year?

As of Jul 15, 2026, the average yearly pay for multimodal learning in Boston, MA is $67,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,500.00 and $78,200.00 per year, depending on experience, location, and employer.

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 are popular job titles related to Multimodal Learning jobs in Boston, MA? For Multimodal Learning jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Multimodal Learning jobs in Boston, MA look for? The top searched job categories for Multimodal Learning jobs in Boston, MA are:
What cities near Boston, MA are hiring for Multimodal Learning jobs? Cities near Boston, MA with the most Multimodal Learning job openings:
Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in Boston, MA as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 25% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $67,022 per year, or $32.2 per hour.
Postdoctoral research fellows in generative, multimodal AI, and seismic foundational models

Postdoctoral research fellows in generative, multimodal AI, and seismic foundational models

Harvard University

Cambridge, MA • On-site

$67K - $80K/yr

Full-time

Posted 15 days ago

Harvard University rating

8.4

Company rating: 8.4 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

81st of 555 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral research fellows in generative, multimodal AI, and seismic foundational models
School
Faculty of Arts and Sciences
Department/Area
Earth and Planetary Sciences
Position Description
Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly motivated Postdoctoral Research Fellow to develop and apply innovative, data-driven models for seismology, with a focus on developing cutting-edge foundation models. This is an exceptional opportunity to contribute to significant scientific discoveries and push the boundaries of AI in Earth science applications.
We are looking for passionate and driven individuals with expertise in one or more of the following areas:
  • Generative AI
  • Agentic AI
  • Graph Representation Learning and Modeling
  • Foundation Models
  • Large Language Models
  • Multimodal Learning
  • Forecasting Models

Basic Qualifications
  • A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field.
  • Demonstrated strong research skills, evidenced by high-quality publications in top-tier machine learning/AI conferences and/or leading scientific journals.
  • Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch).
  • Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure).

Additional Qualifications
  • Experience with multi-GPU model training and large-scale inference.
  • Familiarity with modern AI environments and tools.
  • Prior experience applying AI to seismology or related Earth science domains.

Special Instructions
Contact Information
Corinne Engber
20 Oxford St.
Cambridge, MA 02138
Contact Email
cengber@fas.harvard.edu
Salary Range
$67,600-$80,000
Pay offered to the selected candidate is dependent on factors such as years of experience, training or qualification, field of scholarship, and accomplishments in the field.
Minimum Number of References Required
3
Maximum Number of References Allowed
3
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