ZipRecruiter

Log In

1

Vision Language Model Jobs (NOW HIRING)

Nvidia

Senior Vision Language Model Engineer

Santa Clara, CA

$122.70K - $168.50K/yr

We are seeking a senior vision language model engineer to design and build agentic data and training workflows for Autonomous Vehicles, Robotics, and Medical applications. The right person for this ...

NVIDIA

Senior Vision Language Model Engineer

Santa Clara, CA · On-site

$121.80K - $167.20K/yr

We are seeking a senior vision language model engineer to design and build agentic data and training workflows for Autonomous Vehicles, Robotics, and Medical applications. The right person for this ...

Nvidia

OR · Hybrid

With demand for AI exploding, particularly in the realm of large language models (LLMs) and vision language models (VLMs, VLAs), we are significantly expanding our team. We're seeking a highly ...

next page

Showing results 1-20

People also search for

All JobsVision Language Model Jobs

Vision Language Model information

See salary details

$10

$31

$67

How much do vision language model jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for vision language model in the United States is $31.37, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $39.18 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Vision Language Model Engineer, and why are they important?

To thrive as a Vision Language Model Engineer, you need a strong background in computer vision, natural language processing, machine learning, and often a graduate degree in computer science or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, experience with large-scale datasets, and familiarity with model deployment tools are typically required. Strong problem-solving skills, creativity, and effective collaboration abilities help you stand out in this rapidly evolving field. These skills are essential for developing advanced AI systems that accurately interpret and generate language grounded in visual data, driving innovation in applications like image captioning and visual question answering.

What are some common challenges faced by professionals working with Vision Language Models, and how can they be addressed?

Professionals working with Vision Language Models often encounter challenges such as aligning visual and textual data, handling large-scale datasets, and ensuring model interpretability. Dealing with noisy or incomplete data from either modality can affect model performance, so strong data preprocessing and augmentation skills are essential. Collaboration with multidisciplinary teams—including data engineers, machine learning researchers, and domain experts—is key to refining models and deploying them effectively. Staying updated with the latest advancements and leveraging open-source resources can also help address these challenges.

What is a Vision Language Model?

A Vision Language Model (VLM) is an artificial intelligence system designed to understand and generate information using both visual data (like images or videos) and textual data (like written language). These models are trained on large datasets containing images paired with descriptive text, allowing them to perform tasks such as image captioning, visual question answering, and multimodal content generation. VLMs use advanced machine learning techniques to learn the relationships between visual elements and language, making them valuable for applications that require an integrated understanding of both modalities. They are widely used in fields such as robotics, accessibility technology, and automated content creation.

What is the difference between Vision Language Model vs Computer Vision Engineer?

AspectVision Language ModelComputer Vision Engineer
Required credentialsAdvanced degrees in AI, Machine Learning, or related fieldsDegree in Computer Science, Electrical Engineering, or related fields
Work environmentResearch labs, AI startups, tech companies focusing on multimodal AITech companies, research institutions, industries applying image analysis
Industry usageDeveloping multimodal AI systems combining vision and languageCreating algorithms for image recognition, object detection, and analysis
Search and comparison intentUnderstanding roles in AI development involving vision and languageFocus on technical image processing and computer vision applications

While both roles involve working with visual data, a Vision Language Model specializes in integrating visual and textual information using advanced AI techniques, often in research or product development. In contrast, a Computer Vision Engineer focuses on developing algorithms for analyzing and interpreting visual data, primarily in applications like image recognition and object detection.

Infographic showing various Vision Language Model job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 39% Full Time, 55% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution, with an average salary of $65,246 per year, or $31.4 per hour.

Research Scientist - Vision Language Model

Institute of Foundation Models

Sunnyvale, CA • On-site

Full-time

Posted 5 days ago

Job description

About the Institute of Foundation Models

We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. 

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.

Position Summary
As a Research Scientist in the Vision Language Model (VLM) team, your role will be central to advancing state-of-the-art multimodal foundation models that integrate visual understanding, reasoning, and agentic capabilities. You will work on the research and development of large-scale VLM systems, spanning model architectures, data recipes for pre-training and post-training, and evaluation benchmarks. The role combines cutting-edge research with practical engineering, emphasizing large-scale data processing, filtering, and weighting pipelines, distributed training systems, and reinforcement learning algorithms and systems for multimodal reasoning and agent development.
Key Responsibilities

  • Research and development of next-generation Vision Language Models across pre-training, instruction tuning, reasoning, and agents.

  • Develop novel architectures and training methodologies for integrating visual understanding, language reasoning, and tool-use capabilities.

  • Research efficient multimodal learning techniques, including data-efficient training, long-context modeling, model modularity, and inference optimization.

  • Build and improve large-scale multimodal datasets, synthetic data generation pipelines, and evaluation benchmarks for VLM capabilities.

  • Investigate multimodal reasoning, agentic behavior, OCR, grounding, document understanding, chart understanding, and visual question answering capabilities.

  • Contribute to technical reports, research publications, and open-source software.

  • Represent MBZUAI at research conferences and industry events, showcasing advancements in multimodal foundation models and large-scale AI systems.

  • Mentor junior researchers and collaborate across teams to drive impactful research initiatives.

Academic Qualifications
PhD or equivalent research experience in Machine Learning, Computer Vision, Natural Language Processing, or Multimodal AI.
Salary Range
 
The posted salary range represents the company’s good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.  

Professional ExperienceMinimum  

  • Experience working with large language models and/or vision-language models, including pre-training, fine-tuning, evaluation, or inference.

  • Strong Python and PyTorch development skills for large-scale machine learning research.

  • Experience with distributed training systems and large-scale model optimization.

  • Familiarity with multimodal datasets and data processing pipelines involving images, text, and video.

  • Understanding of modern deep learning architectures, including Transformers, attention mechanisms, and multimodal fusion techniques.

  • Experience with ML infrastructure, including model evaluation, debugging, optimization, and large-scale experimentation.

  • Problem-solving and research skills with the ability to independently drive research/engineering projects.

  • Effective communication and collaboration skills for working across research and engineering teams.


Preferred Skills

  • Hands-on experience training or fine-tuning large Vision Language Models or multimodal foundation models at scale.

  • Experience with distributed learning frameworks and infrastructure such as PyTorch Distributed, Megatron, Triton, or CUDA.

  • Research experience in multimodal reasoning, agentic systems, tool use, OCR, grounding, document understanding, or multimodal retrieval.

  • Experience with synthetic data generation, multimodal data curation, or automated evaluation frameworks for VLMs.

  • Familiarity with efficient training and inference techniques such as FlashAttention, quantization, tensor parallelism, pipeline parallelism, or memory optimization.

  • Experience contributing to open-source ML software and large-scale research codebases.

  • Strong publication record in leading AI conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, or related venues.

  • Experience collaborating across research, infrastructure, and product-oriented teams to deliver state-of-the-art multimodal systems.