Member of Technical Staff - Imagine Model

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: 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 pretraining and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences. RESPONSIBILITIES: • 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. BASIC QUALIFICATIONS: • Track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling. • Experience in data-driven experiment designs, systematic analysis, and iterative model debugging. • Experience developing or working with large-scale distributed machine learning systems. • Ability to deliver optimal end-to-end user experiences. • Hands-on contributor with initiative, excellence, strong work ethic, prioritization skills, and excellent communication. PREFERRED SKILLS AND EXPERIENCE: • 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. • Domain expertise 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 (audio/speech processing or music/audio generation experience is a plus where it supports video). • Experience with agentic RL training, controllable/long-horizon generation, or multimodal agents that reason and act across modalities (especially in visual domains). COMPENSATION AND BENEFITS: $180,000 - $440,000 USD Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks. SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.