We're looking for a Vision AI Engineer to help build nextgeneration video intelligence systems powered by modern visionlanguage models. You'll work across the full video understanding stackcombining multimodal foundation models with established analytics approaches to deliver reliable, productionready AI solutions.
Key Responsibilities
- Build endtoend video analytics pipelines using visionlanguage models.
- Finetune and adapt foundation models for domainspecific video understanding.
- Integrate VLM reasoning with traditional video analytics components.
- Develop and maintain inference pipelines for video and multimodal data.
- Deploy and optimize models for scalable, highperformance production use.
- Diagnose model issues and strengthen system stability and robustness.
- Collaborate with product and engineering teams to deliver AI-driven features.
Required Qualifications
- Strong background in computer vision, video analytics, or AI engineering.
- Practical experience with visionlanguage and videolanguage architectures.
- Hands-on experience finetuning, evaluating, and deploying deep learning models.
- Familiarity with foundation models such as CLIPbased architectures, BLIP/BLIP2, and opensource VLMs (e.g., QwenVL, InternVL).
- Proficiency in Python and deep learning frameworks (e.g., PyTorch).
- Solid understanding of CNNs, Transformers, and attention mechanisms.
- Experience with model optimization techniques (quantization, batching, memory strategies).
- Experience deploying models on Docker, cloud platforms, or onprem GPU systems.
Preferred Qualifications
- Master's or PhD in Computer Vision, Machine Learning, AI, or related fields.
- Experience with realtime or nearrealtime video analytics.
- Familiarity with traditional VA methods (detection, tracking, motion analysis).
- Exposure to MLOps workflows (versioning, CI/CD, monitoring).
- Interest in modern VLM and video understanding research.
What We Offer
- Opportunities to work on cuttingedge multimodal AI technologies.
- Ownership of productionscale video intelligence pipelines.
- A collaborative environment that blends research and engineering.
