- Страна
- США
- Зарплата
- 180 000 $ – 440 000 $
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Member of Technical Staff - Multimodal Understanding
Исключительная возможность работать в одной из самых амбициозных ИИ-компаний мира с доступом к огромным вычислительным ресурсам. Высокая зарплата и значительный пакет акций компенсируют экстремальные требования к работе.
Сложность вакансии
Чрезвычайно высокая сложность из-за требований к экспертному уровню владения JAX/PyTorch, опыту работы с петабайтными данными и распределенными системами обучения моделей с триллионами параметров. Работа в xAI подразумевает высокую интенсивность и готовность брать на себя полную ответственность за результат.
Анализ зарплаты
Предлагаемая базовая зарплата ($180k - $440k) находится на верхнем пределе рынка для Senior/Staff ML-инженеров в США, особенно учитывая, что это только базовая часть без учета акций.
Сопроводительное письмо
I am writing to express my strong interest in the Member of Technical Staff - Multimodal Understanding position at xAI. With a deep background in developing large-scale distributed ML systems and a passion for pushing the boundaries of multimodal intelligence, I am drawn to xAI’s mission of creating AI that truly understands the universe. My experience in optimizing JAX-based training pipelines and managing petabyte-scale data curation aligns perfectly with your team's focus on engineering excellence and hands-on contribution.
In my previous roles, I have successfully led initiatives in cross-modal alignment and spatial-temporal compression, directly improving model reasoning capabilities. I thrive in high-intensity, flat organizations where initiative is rewarded and technical depth is a requirement. I am eager to bring my expertise in Python, Rust, and large-scale orchestration to the multimodal team to help deliver superhuman AI experiences.
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Откликнитесь в xai уже сейчас
Присоединяйтесь к xAI, чтобы создавать ИИ-системы будущего и работать на переднем крае мультимодальных технологий!
Описание вакансии
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 ourRecruitment Privacy Notice.
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Навыки
- C++
- Python
- Rust
- PyTorch
- Machine Learning
- Kubernetes
- JAX
- Computer Vision
- Distributed Systems
- Spark
- Ray
- Natural Language Processing
- Reinforcement Learning
- Multimodal Learning
- XLA
Возможные вопросы на собеседовании
Проверка опыта работы с распределенным обучением на огромных масштабах.
Расскажите о самом сложном случае оптимизации GPU/TPU утилизации, с которым вы сталкивались при обучении модели с миллиардами параметров.
Оценка навыков работы с данными для мультимодальных моделей.
Как вы подходите к фильтрации и обеспечению качества в зашумленных наборах видео-данных для обучения world models?
Проверка понимания архитектурных особенностей мультимодальности.
В чем заключаются основные сложности при выравнивании (alignment) токенизаторов для разных модальностей (текст, видео, аудио)?
Оценка навыков работы с RL и оценкой моделей.
Как бы вы спроектировали систему вознаграждения (reward model) для оценки агентного поведения ИИ в реальном времени?
Проверка соответствия культуре xAI (hands-on подход).
Опишите ситуацию, когда вам пришлось самостоятельно реализовывать решение «от и до» — от сбора данных до деплоя инференса.
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- Страна
- США
- Зарплата
- 180 000 $ – 440 000 $