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ML Features Solutions Engineer

Оценка ИИ

Исключительная вакансия в одной из самых перспективных AI-компаний (SambaNova). Работа с уникальным стеком «от чипа до модели», отличный соцпакет и возможность влиять на индустрию корпоративного ИИ.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена требованиями к глубоким знаниям архитектуры трансформеров, оптимизации инференса (KV cache, квантование) и опыта работы с кастомным оборудованием. Требуется степень магистра или PhD и более 5 лет профильного опыта.

Анализ зарплаты

Медиана215 000 $
Рынок175 000 $ – 260 000 $
Оценка ИИ

Зарплата в объявлении не указана, но для позиций уровня Senior ML Engineer в Bay Area и Остине рыночный диапазон составляет $180,000–$250,000 плюс значительный пакет опционов. Предложение SambaNova, вероятно, находится в верхней части рынка из-за сложности задач.

Сопроводительное письмо

I am writing to express my strong interest in the ML Features Solutions Engineer position at SambaNova Systems. With over 5 years of experience in ML engineering and a deep focus on large language models, I have consistently worked at the intersection of research and production. My expertise in PyTorch, combined with hands-on experience in model optimization techniques such as quantization and efficient inference, aligns perfectly with your goal of delivering production-grade capabilities on the SN40L chip.

In my previous roles, I have successfully translated complex ML research into scalable product features, including structured output generation and inference performance tuning. I am particularly impressed by SambaNova's full-stack approach and the SambaNova Suite's ability to provide enterprise-grade generative AI. I am eager to bring my skills in LLM inference optimization and my passion for high-performance AI hardware to your Product and Solution Engineering team to help accelerate the time-to-market for your next-generation features.

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Описание вакансии

The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale.

SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets.

About the Role

We are seeking an ML Features Solutions Engineer to join our Product and Solution Engineering team, driving the development and optimization of core ML features for enterprise deployment. This role combines deep ML expertise with hands-on engineering, working at the intersection of ML research and product development to deliver production-grade capabilities to our customers.

This role is critical for accelerating ML feature development and bridging the gap between ML research and product engineering and will be driving the following:

  • Core ML Feature Development: Drive improvements to ML features including model optimization, inference performance, and feature enhancements.
  • Production-Ready Solutions: Build and deploy production-ready ML solutions for enterprise customers with focus on reliability and scale.
  • Research to Product Bridge: Translate ML research innovations into practical product features and customer-facing capabilities.
  • Cross-Team Collaboration: Work closely with SDK, testing, and customer teams to ensure ML features meet enterprise requirements.
  • Impact: Accelerates ML feature development and optimization, enabling faster time-to-market for new capabilities while ensuring enterprise-grade quality and performance.

Responsibilities

  • Design and implement core ML features including model optimization, quantization, and inference enhancements
  • Optimize model performance for latency, throughput, and memory efficiency on SambaNova hardware
  • Develop and improve features such as Function Calling, Structured Output, and JSON mode conformance
  • Create end-to-end ML solutions that showcase platform capabilities and accelerate customer adoption
  • Convert cutting-edge ML research into practical, deployable product features
  • Establish benchmarks and quality standards for ML features in production environments
  • Work with SDK team to ensure ML features are properly exposed and documented for developers
  • Support enterprise customers implementing advanced ML features in their workflows
  • Partner with ML research, platform engineering, and customer teams

Required Qualifications

  • Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, or related field
  • 5+ years of industry experience in ML engineering or applied ML research
  • 3+ years of hands-on experience with large language models and transformer architectures
  • Expert proficiency in Python and deep learning frameworks: PyTorch (required), TensorFlow, or JAX
  • Experience with model optimization techniques: quantization, pruning, distillation, efficient inference
  • Strong understanding of LLM inference optimization: KV cache, batching strategies, memory management
  • Experience deploying ML models to production at scale
  • Track record of translating research concepts into production features

Preferred Qualifications

  • PhD in Machine Learning, NLP, or related field
  • Experience with custom hardware acceleration (TPUs, custom ASICs)
  • Hands-on experience with inference frameworks: vLLM, TensorRT-LLM, or similar
  • Experience with function calling and tool use in LLMs
  • Knowledge of structured generation and constrained decoding
  • Experience with ML feature development in enterprise contexts
  • Contributions to open-source ML projects

What We Offer

  • Work on cutting-edge ML features powering the fastest AI inference platform
  • Direct impact on product capabilities used by enterprise customers globally
  • Collaborate with world-class ML researchers and engineers
  • Bay Area location enabling close collaboration with core ML teams
  • Competitive compensation and benefits
  • Opportunity to shape the future of enterprise AI

Submission GuidelinesPlease note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. 

EEO PolicySambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Benefits Summary for US-Based, Full-Time Employment Positions

SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.

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Навыки

  • Python
  • PyTorch
  • TensorFlow
  • JAX
  • Large Language Models
  • Transformers
  • Quantization
  • Model Optimization
  • Inference Optimization
  • vLLM
  • TensorRT-LLM
  • JSON
  • Machine Learning

Возможные вопросы на собеседовании

Проверка глубокого понимания оптимизации работы LLM на уровне железа.

Как бы вы оптимизировали использование KV-кэша для увеличения пропускной способности инференса при ограниченной памяти GPU/ASIC?

Оценка навыков реализации сложных функций LLM, упомянутых в описании.

Опишите ваш подход к реализации надежного Structured Output (JSON mode). Какие методы валидации и констрейнтов вы бы использовали?

Проверка опыта в квантовании моделей.

В чем разница между PTQ и QAT применительно к LLM, и какие артефакты могут возникнуть при агрессивном 4-битном квантовании?

Оценка умения переносить теорию в практику.

Расскажите о случае, когда вы адаптировали свежую исследовательскую статью по ML для внедрения в продакшн. С какими трудностями столкнулись?

Проверка навыков работы с фреймворками глубокого обучения.

Как реализовать кастомный оператор в PyTorch для специфического слоя, чтобы обеспечить максимальную производительность на нестандартном ускорителе?

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