Machine Learning Engineer (Video Foundation Models & Agentic AI)

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