About the role
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, fine-tuning, or inference.
- Proficiency in Python and deep learning frameworks such as PyTorch.
- Familiarity with modern LLMs, multimodal models, or computer vision models.
- Strong problem-solving skills and the ability to independently implement, debug, and optimize machine learning systems.
- Ability to work in a fast-paced environment, take ownership, and deliver practical solutions to complex problems.
Preferred Qualifications
- Strong research capabilities, demonstrated through publications at top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) or other impactful research contributions.
- Experience with video understanding, vision-language models, or multimodal foundation models.
- Experience with LLM/VLM post-training, including SFT, reinforcement learning, or reasoning-oriented training.
- Experience with distributed training, efficient inference, or production-scale ML systems.
- Familiarity with agentic AI, tool-augmented reasoning, or multimodal reasoning.
- Proven ability to translate novel research ideas into practical implementations and measurable improvements.
Why Join Us?
- Real-World Impact: Build video AI systems that directly power real-world products and reach users through production deployment.
- End-to-End Ownership: Work across the complete ML lifecycle, from data and model development to optimization and deployment, with the opportunity to own meaningful technical initiatives.
- Challenging MIL Problems: Solve practical and technically challenging problems in video understanding, large-scale model training, and efficient inference.
- Frontier AI Exploration: Stay close to advances in multimodal foundation models, reasoning, reinforcement learning, and agentic AI, with opportunities to apply new ideas to real-world problems.
- Startup Environment: Work in a small, fast-moving team that values ownership, technical excellence, rapid iteration, and measurable impact.
Default Benefits
- Health insurance
- Vision care
- Dental coverage
- 401(k)
- Paid holidays
- PTO (Paid Time Off)
- Sick leave