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Multimodal Learning Jobs in Massachusetts (NOW HIRING)

Abbott

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Experience with foundation models, generative AI, self-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging. * Expertise in model deployment and ...

Abbott

Staff AI/ML Engineer

Westford, MA

$99K - $198K/yr

Experience with foundation models, generative AI, self-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging. * Expertise in model deployment and ...

Abbott

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Experience with foundation models, generative AI, self-supervised learning, multimodal learning, or other advanced AI approaches applied to medical imaging. * Expertise in model deployment and ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... Design and implement efficient and scalable MLLM models for inference and analysis of multimodal ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Design and implement efficient and scalable MLLM models for inference and analysis of multimodal ... Learning & Development programs * And yes, we have snacks in our offices Benefits listed herein may ...

VideaHealth

Senior Machine Learning Engineer

Boston, MA · On-site

$170K - $205K/yr

About the position: We're looking for a Senior Machine Learning Engineer with deep expertise in ... Depending on your background, that might mean predictive and tabular modeling, multimodal systems ...

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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 Massachusetts?

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

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

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

What cities in Massachusetts are hiring for Multimodal Learning jobs?

Cities in Massachusetts with the most Multimodal Learning job openings:

Multimodal Learning jobs near you

ML Research Scientist I/II, Multimodal Data Extraction

Lila Sciences

Cambridge, MA

Full-time

Re-posted 22 days ago

Job description

Your Impact at LILA

As a ML Research Scientist - Multimodal Data Extraction, you will advance Lila's vision of scientific superintelligence by developing foundation models that autonomously read, interpret, and structure scientific knowledge across text, images, and experimental data in the physical sciences. Your research will help unify the world's scientific information into machine-understandable form, powering reasoning, prediction, and autonomous discovery across materials science and chemistry.

What You'll Be Building

  • Research and develop AI systems that extract and structure knowledge from diverse scientific sources.
  • Design and fine-tune large language, multi-modal and specialized models for factual, interpretable data extraction.
  • Build scalable pipelines for unstructured and heterogeneous scientific data, integrating text, tables, and visuals.
  • Collaborate with domain experts to align extracted data with real-world discovery workflows.
  • Publish research that advances the state of the art in multimodal understanding and AI-driven knowledge extraction.

What You'll Need to Succeed

  • PhD (or equivalent research experience) in Computer Science, Chemistry, Materials Science, or related field.
  • Expertise in machine learning, NLP, and vision-language modeling using PyTorch and Hugging Face Transformers.
  • Proven ability to train, fine-tune, and evaluate LLMs and multimodal models for scientific data extraction.
  • Strong understanding of data structures and representations used in the physical sciences.
  • Demonstrated research impact through publications, preprints, or open-source work (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, Scientific Journals).

Bonus Points For

  • Experience with multimodal fusion architectures and document-level understanding.
  • Knowledge of scientific document parsing (OCR, table extraction, figure-caption linking).
  • Familiarity with knowledge graph construction or reasoning systems for science.
  • Experience with noisy or heterogeneous real-world scientific data.
  • Collaborative mindset and passion for advancing AI in the physical sciences.

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