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Multimodal Learning Jobs in Virginia (NOW HIRING)

... learning. * Previous experience negotiating milestone-based Statements of Work contracts. Preferred Qualifications * Demonstrated expertise in applied AI/ML, generative and multimodal AI, enterprise ...

... learning. * Previous experience negotiating milestone-based Statements of Work contracts. Preferred Qualifications * Demonstrated expertise in applied AI/ML, generative and multimodal AI, enterprise ...

Technology Architect - AI

Mclean, VA · On-site

$138K - $180K/yr

... learning. * Previous experience negotiating milestone-based Statements of Work contracts. Preferred Qualifications * Demonstrated expertise in applied AI/ML, generative and multimodal AI, enterprise ...

Software Engineer

Sterling, VA · On-site

$99K - $206K/yr

Work with LLMs and multimodal models through APIs or on-premise deployments. * Develop and maintain machine learning pipelines and applications using Python and modern ML frameworks. * Implement and ...

Software Engineer

Sterling, VA · On-site

$99K - $206K/yr

Work with LLMs and multimodal models through APIs or on-premise deployments. * Develop and maintain machine learning pipelines and applications using Python and modern ML frameworks. * Implement and ...

Innovation & Continuous Learning * Stay current with emerging AI technologies (e.g., agentic AI, multimodal systems). * Contribute to AI Center of Excellence initiatives and innovation programs.

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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 cities in Virginia are hiring for Multimodal Learning jobs?

Cities in Virginia with the most Multimodal Learning job openings:

Infographic showing various Multimodal Learning job openings in Virginia as of August 2026, with employment types broken down into 67% Full Time, and 33% Temporary. Highlights an 100% In-person job distribution.

Senior Machine Learning Research Scientist - Frontier Lab

Arlington, VA • On-site


Carnegie Mellon University
Colleges, Universities, and Professional Schools • 1 - 10 employees

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

70th of 627 rated colleges and universities

Great coworkers

People enjoy working here

Good employer


$113K - $144K/yr

Full-time

Re-posted 12 days ago


Job description

Job Summary:
Carnegie Mellon University is seeking a Senior Machine Learning Research Scientist in the Frontier Lab, which focuses on applied artificial intelligence for government missions. The role involves leading technical execution, conducting applied research, and developing prototypes while collaborating with stakeholders to translate mission needs into actionable technical outcomes.
Responsibilities:
• Execute work within the operational context—understanding users, workflows, constraints, success criteria, and outcomes—so technical decisions are grounded in real mission needs.
• Lead technical execution by defining technical tasking, sequencing work into realistic milestones, maintaining delivery quality, and delegating appropriately across the team.
• Design and run studies, build convincing prototypes and reference implementations, and produce evidence-backed insights that can be matured and transitioned into operational settings.
• Establish credible evaluation strategies and test pipelines that assess performance, robustness, reliability, and trustworthiness in mission-representative scenarios.
• Serve as the primary technical interface when appropriate; translate mission goals into measurable technical outcomes; communicate progress, decisions, and risks clearly to stakeholders.
• Proactively mentor junior staff and teammates, raising the bar for research rigor, engineering practice, and delivery habits across project teams.
• Maintain strong awareness of frontier developments aligned to the Frontier Lab, share insights with the lab, and help shape research directions and future work selection.
• Manage multiple priorities effectively, sustain steady execution cadence, and resolve blockers with minimal oversight.
• Build a strong research culture through internal talks, reading groups, and workshops; and engage with external AI/ML communities (professional societies, consortiums, working groups, and conferences) to strengthen collaboration pathways and keep the lab connected to emerging practice.
Qualifications:
Required:
• BS in Computer Science, Electrical Engineering, Statistics, or related field with 10 years of relevant experience; OR MS with 8 years of relevant experience; OR PhD with 5 years of relevant experience.
• Deep expertise in one or more Frontier Lab-aligned areas (agentic systems, LLM reliability/evaluation, CV evaluation, robustness/assurance, TEVV pipelines, multimodal learning, edge ML).
• Strong engineering capability – can build and maintain high-quality prototypes, evaluation infrastructure, and repeatable experimentation workflows.
• Strong written and verbal communication skills; able to represent technical work credibly to senior stakeholders.
• Demonstrated ability to lead technical workstreams and coordinate multi-person execution.
• Flexible to travel to SEI offices in Pittsburgh, PA and Washington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel).
• You must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week.
• You will be subject to a background investigation and must be eligible to obtain and maintain a Department of War security clearance.
Preferred:
• Leading applied research projects resulting in effective prototypes, mission-relevant evaluation outcomes, or transitioned methods.
• Publications at strong venues (e.g., NeurIPS / ICLR / ICML, relevant workshops, MLCON), and/or demonstrable impact through applied research artifacts (benchmarks, evaluation suites, open-source, technical reports).
• Designing and operating TEVV efforts including evaluation pipelines, robustness analysis, calibration/uncertainty work, regression suites, and scenario-based evaluation protocols.
• Building agentic capabilities integrated with tools, data systems, and human workflows (decision support, planning, analytic contexts).
• Experience with secure or operational environments and delivery constraints typical of government settings.
• Experience shaping a technical roadmap or research portfolio aligned to sponsor priorities and lab strategy.
Company:
Carnegie Mellon University is a research university offering programs and research across engineering, science, arts, and business. Founded in 1900, the company is headquartered in Pittsburgh, USA, with a team of 5001-10000 employees. The company is currently Late Stage.


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