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xAIPosted Nov 6, 2026
Model Engineer, Multimodal Understanding
Palo Alto, CAfull timeOn Site$220k–$357kH1B: no
xAI Model Engineer role on the multimodal team to advance superhuman multimodal intelligence across image, video, audio, and text. Responsibilities include designing large-scale distributed systems, developing data pipelines, and innovating algorithms for state-of-the-art performance.
Responsibilities
- 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, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance
Qualification
Experience training large models (e
Required
- Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
- Experience training large models (e.g., pretraining, post-training, fine-tuning, or RL training of neural networks with 1B+ parameters)
Preferred
- 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)
Benefits
- Dental
- Vision
- 401(k)
- Equity/RSU
- Life Insurance
Company
Source: greenhouse
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