JOB DESCRIPTION:
ABOUT THE TEAM
We are building Max VLM, a next-generation video foundation model designed to understand human activities, object interactions, and the physical world through first-person video.
Our mission is to develop intelligent video understanding systems that can accurately interpret complex real-world activities and turn continuous visual observations into structured, actionable information.
As a fast-moving startup team, we work across the entire machine learning lifecycle, from data collection and model training to evaluation, optimization, and production deployment. We combine state-of-the-art multimodal AI technologies with real-world data and practical engineering to build scalable, reliable, and efficient AI systems.
PROJECT OVERVIEW & TECHNICAL CHALLENGES
Despite rapid advances in multimodal large language models, understanding real-world video remains a challenging problem. Existing systems often struggle with fine-grained activity recognition, temporal reasoning, long-video comprehension, and computational efficiency.
Our team focuses on building high-performance video foundation models and bringing them into real-world applications. Key technical directions include:
1 - VIDEO FOUNDATION MODELS & MULTIMODAL UNDERSTANDING
Develop and improve video-language models capable of understanding complex human activities and physical-world interactions.
- Fine-grained action recognition, temporal grounding, and video understanding.
- Long-video comprehension, temporal reasoning, and event-level understanding.
- Multimodal model fine-tuning, post-training, and performance optimization.
- High-quality training data generation, automated labeling, and data selection.
- Model evaluation, error analysis, and data-driven iteration to continuously improve production performance.
2 - EFFICIENT MODEL TRAINING & DEPLOYMENT
Build scalable and efficient machine learning systems that support real-world product requirements.
- Large-scale model training, fine-tuning, and distributed training optimization.
- Efficient video processing, and inference acceleration.
Model compression, quantization, and optimization of latency, throughput, and computational cost.
- Production model serving, monitoring, and continuous performance improvement.
- End-to-end optimization of model quality, reliability, and deployment efficiency.
3 - Agentic Video Understanding & Advanced Post-Training
Explore advanced AI techniques to improve video understanding and reasoning capabilities.
- Develop agentic video understanding systems with autonomous tool use and multi-step reasoning.
- Explore reinforcement learning and advanced post-training techniques to improve model performance.
- Apply emerging AI research to real-world applications and product improvements.
KEY RESPONSIBILITIES
- Design, train, fine-tune, and optimize video-language foundation models for real-world applications.
- Build and improve large-scale ML pipelines covering data processing, training, evaluation, and inference.
- Analyze model failures, identify performance bottlenecks, and drive continuous improvements through data and algorithmic iteration.
- Develop efficient model inference and deployment solutions that balance quality, latency, and computational cost.
- Collaborate closely with product and engineering teams to translate business requirements into technical solutions and deliver production-ready ML capabilities.
- Explore and evaluate emerging techniques in multimodal learning, reinforcement learning, and agentic AI, applying promising advances to practical problems.
- Take ownership of ML projects from problem definition and experimentation through deployment and ongoing improvement.
QUALIFICATIONS
MINIMUM QUALIFICATIONS
- Bachelor's, Master's, or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent practical experience.
- Strong understanding of deep learning and hands-on experience with model training,…