Model Engineer, Multimodal Understanding

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 interactionsBASIC QUALIFICATIONS:Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++PREFERRED SKILLS AND EXPERIENCE:Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLAProven 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 Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems Proficiency in Rust and/or C++ for performance-critical components Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership Passion for end-to-end user experience in interactive, real-time multimodal AI systemsCOMPENSATION AND BENEFITS:$160,000 - $250,000 USDBase 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.