Machine Learning Engineer

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.

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:

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.

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.

• 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.

• Bachelors Masters 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.

• 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.