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SeniorВ офисеПолная занятость

Staff Backend Software Engineer- (AI Platform)

Оценка ИИ

Databricks — лидер индустрии, предлагающий работу над передовыми AI-технологиями. Высокая зарплата, отличные бонусы и возможность влиять на продукт мирового уровня делают эту вакансию крайне привлекательной.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль уровня Staff требует более 10 лет опыта и глубоких знаний в распределенных системах. Высокая сложность обусловлена необходимостью оптимизации GPU-нагрузок и работы с низкоуровневыми оптимизаторами (vLLM, SGLang).

Анализ зарплаты

Медиана235 000 $
Рынок190 000 $ – 280 000 $
Оценка ИИ

Предложенная зарплата ($192k - $260k) полностью соответствует рыночным стандартам для позиции Staff Engineer в Сан-Франциско. С учетом бонусов и опционов совокупный доход может значительно превышать медиану.

Сопроводительное письмо

I am writing to express my strong interest in the Staff Backend Software Engineer position within the AI Platform team at Databricks. With over a decade of experience in architecting and operating high-scale distributed systems, I have a proven track record of delivering low-latency, mission-critical backend services. My background in building robust API gateways and handling complex operational challenges aligns perfectly with your mission to provide seamless Foundation Model Serving for frontier AI models.

Throughout my career, I have focused on technical excellence and mentoring engineering teams to achieve high-impact results. I am particularly drawn to this role because it offers the opportunity to apply deep systems engineering principles to the rapidly evolving field of LLM runtimes and GPU workloads. I am eager to bring my expertise in scalability and reliability to Databricks to help shape the next generation of data and AI infrastructure.

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Откликнитесь в databricks уже сейчас

Присоединяйтесь к команде Databricks и создавайте будущее AI-инфраструктуры мирового уровня!

Описание вакансии

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

Foundation Model Serving is the API Product for hosting and serving frontier AI model inference for open source models like Llama, Qwen, and GPT OSS as well as proprietary models like Claude and OpenAI GPT. For this role, no prior ML or AI experience is necessary. We’re looking for engineers who have owned high scale operational sensitive systems like customer facing APIs, Edge Gateways, ML Inference, or similar services and have an interest in getting deep building LLM APIs and runtimes at scale.

As a Staff Engineer, you’ll play a critical role in shaping both the product experience and core infrastructure. You will design and build systems that enable high-throughput, low-latency inference on GPU workloads with frontier models, influence architectural direction, and collaborate closely across platform, product, infrastructure, and research teams to deliver a world-class foundation model API product.

The impact you will have:

  • Design and implement core systems and APIs that power Databricks Foundation Model Serving, ensuring scalability, reliability, and operational excellence.
  • Partner with product and engineering leadership to define the technical roadmap and long-term architecture for serving workloads.
  • Drive architectural decisions and trade-offs to optimize performance, throughput, autoscaling, and operational efficiency for GPU serving workloads.
  • Contribute directly to key components across the serving infrastructure — from working in systems like vLLM and SGLang to creating token based rate limiters and optimizers — ensuring smooth and efficient operations at scale.
  • Collaborate cross-functionally with product, platform, and research teams to translate customer needs into reliable and performant systems.
  • Establish best practices for code quality, testing, and operational readiness, and mentor other engineers through design reviews and technical guidance.
  • Represent the team in cross-organizational technical discussions and influence Databricks’ broader AI platform strategy.

What we look for:

  • 10+ years of experience building and operating large-scale distributed systems.
  • Experience leading high-scale operationally sensitive backend systems.
  • A track record of up-leveling teams engineering excellence.
  • Strong foundation in algorithms, data structures, and system design as applied to large-scale, low-latency serving systems.
  • Proven ability to deliver technically complex, high-impact initiatives that create measurable customer or business value.
  • Strong communication skills and ability to collaborate across teams in fast-moving environments.
  • Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.
  • Passion for mentoring, growing engineers, and fostering technical excellence.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range

$192,000—$260,000 USD

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on TwitterLinkedIn and Facebook.

BenefitsAt Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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Навыки

  • Python
  • LLM
  • Microservices
  • Scalability
  • System Design
  • Distributed Systems
  • Java
  • API Design
  • Go
  • Scala
  • GPU
  • vLLM

Возможные вопросы на собеседовании

Проверка опыта работы с высоконагруженными системами, что критично для API обслуживания моделей.

Расскажите о самом сложном случае масштабирования распределенной системы, которым вы руководили. С какими узкими местами вы столкнулись?

Позиция подразумевает работу с GPU-инференсом, где задержки имеют решающее значение.

Какие стратегии вы бы использовали для минимизации задержек (latency) в API, которое обрабатывает тяжелые вычислительные задачи на GPU?

В описании упоминаются токен-лимитеры. Это специфическая задача для LLM.

Как бы вы спроектировали распределенный rate limiter, основанный на количестве токенов, для системы с миллионами запросов?

Роль Staff Engineer предполагает лидерство и наставничество.

Опишите ситуацию, когда вам нужно было убедить руководство или другие команды принять сложное архитектурное решение. Как вы аргументировали свой выбор?

Проверка навыков обеспечения надежности в критических системах.

Как вы подходите к обеспечению операционной стабильности (observability и fault tolerance) при внедрении новых компонентов в работающую высоконагруженную платформу?

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databricks
Страна
США
Зарплата
192 000 $ – 260 000 $