Multimodal AI Researcher

Job Description

Job Description

Multimodal AI Researcher Job Title

Multimodal AI Researcher

Job Summary

We are looking for a highly motivated Multimodal AI Researcher to advance the development of AI systems capable of understanding and reasoning across multiple data modalities, including text, images, audio, video, and structured data. The ideal candidate will have a strong research background in machine learning, deep learning, computer vision, natural language processing, and multimodal foundation models. You will collaborate with research scientists and engineering teams to build innovative AI solutions and translate cutting-edge research into production-ready applications.

Key Responsibilities

• Conduct research on multimodal AI models integrating text, images, audio, video, and other data modalities.

• Design, implement, and evaluate state-of-the-art multimodal architectures, including vision-language models (VLMs), multimodal large language models (MLLMs), and cross-modal retrieval systems.

• Develop and optimize models for tasks such as image captioning, visual question answering (VQA), document understanding, speech-text understanding, video understanding, and multimodal reasoning.

• Fine-tune, evaluate, and benchmark foundation models using domain-specific datasets.

• Design scalable training and inference pipelines for multimodal AI applications.

• Collaborate with machine learning engineers, data scientists, software engineers, and product teams to deploy research into production.

• Conduct experiments, analyze results, and publish technical reports or research papers where applicable.

• Stay current with the latest advancements in multimodal AI, foundation models, and generative AI.

Required Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, or a related field.

• 3 years of experience in AI/ML research or applied machine learning.

• Strong understanding of:

  • Deep Learning

  • Transformer architectures

  • Vision Transformers (ViTs)

  • Contrastive Learning

  • Representation Learning

  • Cross-modal Attention

  • Self-Supervised Learning

  • Foundation Models

• Proficiency in Python.

• Experience with PyTorch or TensorFlow.

• Hands-on experience with Hugging Face Transformers and multimodal model development.

• Strong knowledge of computer vision, natural language processing, and machine learning fundamentals.

• Experience with distributed training and GPU optimization.

• Familiarity with Git, Docker, Linux, and cloud platforms (AWS, Azure, or Google Cloud).

Preferred Qualifications

• Experience with vision-language models such as CLIP, BLIP, LLaVA, Florence, or Qwen-VL.

• Experience with multimodal large language models (MLLMs) and retrieval-augmented generation (RAG).

• Familiarity with speech and audio models, including Whisper or similar architectures.

• Experience working with image, video, document, or medical imaging datasets.

• Publications in top AI conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, or NAACL.

• Experience contributing to open-source AI projects.

Technical Skills

• Python

• PyTorch / TensorFlow

• Hugging Face Transformers

• OpenCV

• CUDA

• DeepSpeed

• Ray

• NumPy

• Pandas

• Git

• Docker

• Kubernetes (preferred)

• Linux

• SQL

• AWS / Azure / Google Cloud

Soft Skills

• Strong research, analytical, and problem-solving skills.

• Excellent communication and technical writing abilities.

• Ability to collaborate across multidisciplinary teams.

• Curiosity and passion for advancing AI research.

• Strong experimentation and debugging skills.

Nice to Have

• Experience with multimodal RAG systems.

• Knowledge of AI agents and agentic workflows.

• Familiarity with vector databases such as Pinecone, Milvus, Weaviate, or FAISS.

• Experience with MLOps, model serving, and CI/CD pipelines.

• Knowledge of reinforcement learning and reinforcement learning from human feedback (RLHF).

• Experience with synthetic data generation and evaluation frameworks.

Benefits

• Competitive salary and performance-based incentives.

• Flexible work arrangements.

• Comprehensive health and wellness benefits.

• Learning and conference sponsorship opportunities.

• Access to high-performance GPU infrastructure.

• Opportunity to work on cutting-edge AI research with a collaborative team.