Sr. Model Engineer, Imagine

As a multimodal engineer on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, with a strong focus on enabling high-fidelity understanding and generation across image and video modalities, while also incorporating audio where it enhances visual content (e.g., synchronized audio for video). Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pre-training and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences.

• →Create and drive engineering agendas to advance multimodal capabilities, with emphasis on image and video generation, editing, understanding, controllable/long-horizon synthesis, agentic planning, RL training, and world simulation (including audio integration for richer video experiences)
• →Improve data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, particularly for visual and audio data
• →Design evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence
• →Implement efficient algorithms for state-of-the-art model performance, including real-time inference, distillation, and scalable serving for visual content
• →Develop scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets
• →Collaborate cross-functionally to integrate AI solutions into production and rapidly iterate based on user feedback

• Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
• Experience in multimodal applications such as graphics engines, rendering techniques, image/video understanding and generation, world models, real-time simulation, or controllable/long-horizon visual content creation

• Experience in audio/speech processing or music/audio generation experience is a plus where it supports video
• Experience in data-driven experiment designs, systematic analysis, and iterative model debugging
• Experience developing or working with large-scale distributed machine learning systems
• Experience with leading studies that significantly improve neural network capabilities and performance through better data or modeling
• Ability to deliver optimal end-to-end user experiences
• Hands-on contributor with initiative, excellence, strong work ethic, prioritization skills, and excellent communication
• Experience in SFT, RL, evals, human/synthetic data collection, or agentic systems
• Proficiency in Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray, and related large-scale frameworks
• Experience with agentic RL training, controllable/long-horizon generation, or multimodal agents that reason and act across modalities (especially in visual domains)