Sr. Model Engineer, Imagine

SpaceXSI•Palo Alto, CA

1d•$220,000 - $357,000

About The Position

As a multimodal engineer on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, focusing on high-fidelity understanding and generation across image and video modalities, and incorporating audio where it enhances visual content, such as synchronized audio for video. The work spans data curation, modeling, training, inference serving, and product integration across pre-training and post-training. You will collaborate closely with product teams to advance model capabilities and deliver exceptional end-to-end user experiences.

Requirements

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

Nice To Haves

Experience in audio or speech processing, or music and audio generation, where it supports video.
Experience in data-driven experiment design, systematic analysis, and iterative model debugging.
Experience developing or working with large-scale distributed machine learning systems.
Experience 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 contribution, initiative, excellence, strong work ethic, prioritization skills, and excellent communication.
Experience in supervised fine-tuning, reinforcement learning, evaluations, human or synthetic data collection, or agentic systems.
Proficiency in Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray, and related large-scale frameworks.
Experience with agentic reinforcement learning training, controllable or long-horizon generation, or multimodal agents that reason and act across modalities, especially in visual domains.

Responsibilities

Create and drive engineering agendas to advance multimodal capabilities, emphasizing image and video generation, editing, understanding, controllable and long-horizon synthesis, agentic planning, reinforcement learning 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, evaluations, and reward models tailored to image, video, and 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 datasets, primarily focused on image and video.
Collaborate cross-functionally to integrate AI solutions into production and rapidly iterate based on user feedback.