Member of Technical Staff - Multimodal at XAI LONDON LTD

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

16 Jun, 26

Salary

440000.0

Posted On

18 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Multimodal Pre-training, Multimodal Post-training, Fine-tuning, Python, JAX, PyTorch, XLA, Distributed ML Systems, Data Pipelines, Evaluation Design, Reward Modeling, RL Techniques, Spatial-Temporal Compression, Cross-Modal Alignment, World Modeling, Tool Use

Industry

technology;Information and Internet

Description

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. ABOUT THE ROLE: 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. 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. BASIC QUALIFICATIONS: 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. PREFERRED SKILLS AND EXPERIENCE: 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 systems. COMPENSATION AND BENEFITS: $180,000 - $440,000 USD Base salary is just one part of our total rewards package at xAI, 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. xAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

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Responsibilities

Responsibilities include designing, building, and optimizing large-scale distributed systems for multimodal pre-training, post-training, and data processing at web/petabyte scale. The role also involves advancing multimodal capabilities like spatial-temporal compression and world modeling, and creating evaluation frameworks and benchmarks.