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Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.\nSpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\nMODEL ENGINEER, MULTIMODAL UNDERSTANDING\nYou will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities-image, video, audio, and text-spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.\nCollaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.\nRESPONSIBILITIES:\nDesign, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale\nDevelop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text)\nAdvance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding & generation, real-time video processing, and noisy data handling\nDrive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models\nCreate evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy\nInnovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance\nBuild research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback\nWork across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions\nBASIC QUALIFICATIONS:\nExperience writing production-quality code in Python, PyTorch, JAX, Rust, or C++\nPREFERRED SKILLS AND EXPERIENCE:\nExpert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA\nProven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design)\nDeep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data\nStrong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors)\nProactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers\nWillingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences\nExperience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling\nFamiliarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems\nProficiency in Rust and/or C++ for performance-critical components\nHands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes\nBackground building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership\nPassion for end-to-end user experience in interactive, real-time multimodal AI systems\nCOMPENSATION AND BENEFITS:\n$160,000 - $250,000 USD\nBase salary is just one part of our total rewards package at SpaceXSI, 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.\nSpaceXSI is an equal opportunity employer. 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