Member of Technical Staff - Multimodal Understanding at SpaceXAI

On-site - Palo Alto, CA

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SpaceXAI is on a mission to build AI systems that can accurately understand the universe and aid humanity. The multimodal team focuses on advancing understanding and generation across modalities—image, video, audio, and text—through data curation, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences. Candidates will collaborate with cross-functional teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration.

Salary

USD 180,000 - 440,000

Requirements

Skills

  • Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal)
  • Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA
  • Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design)
  • Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data
  • Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors)
  • Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers
  • Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences

Responsibilities

  • Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale
  • Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text)
  • Advance 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
  • Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models
  • Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy
  • Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance
  • Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback
  • Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions

Technologies

PythonJAXPyTorchXLARustC++SparkRayKubernetesDistributed ML systemsGPUTPUHardware co-designEvaluation designBenchmarksReward modelingReinforcement LearningData pipelinesTokenizersCompression techniquesReasoningAgentic systems

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