Member of Technical Staff - Multimodal Understanding

Member of Technical Staff - Multimodal Understanding About xAI

xAI’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.

You 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.

Collaborate 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.

• 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, modelling 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 behaviours, orchestration, and seamless real‑time interactions.

• 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 optimisation, GPU utilisation, 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 modelling, or RL techniques (particularly for interactive/agentic behaviours).
• 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, modelling, algorithms, or scaling.
• Familiarity with state‑of‑the‑art in multimodal LLMs, scaling laws, tokenisers, compression techniques,…