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Multimodal Learning Jobs in Park Ridge, IL (NOW HIRING)

Cisco

Applied AI Scientist

Chicago, IL · On-site

... multimodal machinegenerated data - including logs, time series, traces, and events! We combine deep ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

D. in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Statistics, or a ... Experience with knowledge graphs and multimodal LLMs * Familiarity with AI/ML observability tools ...

iManage

Senior AI Engineer

Chicago, IL · On-site

$180K - $220K/yr

D. in Computer Science, Machine Learning, Data Science, Artificial Intelligence, Statistics, or a ... Experience with knowledge graphs and multimodal LLMs * Familiarity with AI/ML observability tools ...

The Hartford

AVP Applied AI

Chicago, IL · On-site +1

The Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning ... multimodal use cases. * Lead and develop Sr. Directors and Directors. Build leadership bench ...

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All JobsMultimodal Learning JobsMultimodal Learning Jobs in Park Ridge, IL

Multimodal Learning information

See Park Ridge, IL salary details

$20.7K

$60.7K

$112.7K

How much do multimodal learning jobs pay per year?

As of Sep 14, 2026, the average yearly pay for multimodal learning in Park Ridge, IL is $60,705.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,300.00 and $70,800.00 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 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 job categories do people searching Multimodal Learning jobs in Park Ridge, IL look for?

The top searched job categories for Multimodal Learning jobs in Park Ridge, IL are:

What cities near Park Ridge, IL are hiring for Multimodal Learning jobs?

Cities near Park Ridge, IL with the most Multimodal Learning job openings:

Multimodal Learning jobs near you

Postdoctoral Appointee - Materials Informatics and Autonomous Synthesis

Lemont, IL • On-site

Argonne National Laboratory
Scientific Research and Development Services • 1 - 5K employees
Learn more about Argonne National Laboratoryexternal

$72K - $121K/yr

Full-time

Re-posted 25 days ago

Job description

The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials discovery and synthesis.
We are seeking a creative and collaborative researcher who is excited by the opportunity to help shape the future of autonomous synthesis and self-driving laboratories. This role is ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real laboratory workflows.
The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and long-term scientific impact.
This research may include applications in areas such as organic electrochemical and neuromorphic devices, but the central emphasis is on creating data-driven methods and infrastructure that can guide experiments, improve efficiency, and strengthen collaboration between computation and experiment.
Key Responsibilities
  • Develop machine learning-ready data resources for materials by integrating literature, in-house, and newly generated experimental data
  • Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and device-relevant properties
  • Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot
  • Work closely with experimental researchers to integrate AI/ML workflows into closed-loop autonomous synthesis, fabrication, and characterization; translate model predictions into experimental campaigns; and update models using newly acquired data
  • Contribute to strategies for generating diverse, high-value datasets, identifying meaningful descriptors and representations, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long-term autonomous laboratory capabilities
  • Share research outcomes through publications, presentations, software, datasets, and internal reports

Position Requirements
  • Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemistry, chemical engineering, materials science, polymer science, physics, computer science, and/or data science
  • Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design
  • Strong Python and scientific computing skills, including experience with tools such as NumPy, pandas, scikit-learn, and machine learning frameworks such as PyTorch, TensorFlow, or similar
  • Experience developing surrogate models, predictive models, or adaptive learning workflows for scientific or engineering applications
  • Strong interest in working closely with experimental researchers in a laboratory-centered environment
  • Evidence of independent research productivity through publications, software, datasets, or similar outputs
  • Excellent communication skills, the ability to work effectively in interdisciplinary teams
  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Preferred Qualifications
  • Experience with active learning, Bayesian optimization, adaptive experimental design, reinforcement learning for experiments, or uncertainty quantification
  • Experience with autonomous, self-driving, or robotic laboratory platforms
  • Background in electronic polymers, conjugated polymers, organic semiconductors, soft materials, electrochemical materials, or related functional materials
  • Experience integrating literature, experimental, and simulation datasets into unified, machine learning-ready workflows
  • Familiarity with cheminformatics or polymer informatics, molecular representations, descriptor engineering, RDKit, characterization-informed modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature
  • Experience with workflow automation, data infrastructure, database development, reproducible research pipelines, and collaborative environments that span computation, data science, and experiment

Application Materials
  • Updated CV/Resume
  • Unofficial Ph.D. transcripts
  • If already awarded, a copy of the Ph.D. diploma

Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
The expected hiring range for this position is $72,879.00-$121,465.00.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
Click here to view Argonne employee benefits!
As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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