- Страна
- США
- Зарплата
- 166 000 $ – 225 000 $
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Staff Backend Software Engineer- (AI Platform)
Исключительная вакансия в одной из самых влиятельных компаний в сфере данных и ИИ. Высокая зарплата, работа с передовыми технологиями и возможность влиять на продукт, которым пользуются тысячи компаний из Fortune 500.
Сложность вакансии
Высокая сложность обусловлена требованиями к глубоким знаниям распределенных систем, оптимизации инференса на GPU/CPU и опытом работы с высоконагруженными ИИ-платформами. Роль уровня Staff подразумевает не только техническое превосходство, но и лидерство в архитектурных решениях.
Анализ зарплаты
Предложенный диапазон $166k–$225k полностью соответствует рыночным стандартам для Staff-инженеров в Сан-Франциско. Учитывая бонусы и опционы, совокупный доход может быть значительно выше среднего по рынку.
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 6 years of experience in developing high-performance distributed systems and a deep focus on model serving infrastructure, I have consistently delivered scalable solutions that handle low-latency inference at scale. My background in optimizing GPU/CPU workloads and building intelligent autoscaling systems aligns perfectly with the goals of your Model Serving product.
At my previous roles, I have led technical initiatives that significantly improved system reliability and reduced operational costs. I am particularly drawn to Databricks because of its leadership in the Data Intelligence Platform space and its commitment to open-source innovation through projects like MLflow. I am eager to bring my expertise in routing, caching, and observability to help Databricks customers operationalize their most complex models with confidence.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в databricks уже сейчас
Присоединяйтесь к команде Databricks и создавайте будущее инфраструктуры ИИ 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.
Databricks’ Model Serving product provides enterprises with a unified, scalable, and governed platform to deploy and manage AI/ML models — from traditional ML to fine-tuned and proprietary large language models. It offers real-time, low-latency inference, governance, monitoring, and lineage. As AI adoption accelerates, Model Serving is a core pillar of the Databricks platform, enabling customers to operationalize models at scale with strong SLAs and cost efficiency.
As a Senior Engineer, you’ll play a critical role in shaping both the product experience and the foundational infrastructure of Model Serving. You will design and build systems that enable high-throughput, low-latency inference across CPU and GPU workloads, influence architectural direction, and collaborate closely across platform, product, infrastructure, and research teams to deliver a world-class serving platform.
The impact you will have:
- Design and implement core systems and APIs that power Databricks Model Serving, ensuring scalability, reliability, and operational excellence.
- Drive architectural decisions and trade-offs to optimize performance, throughput, autoscaling, and operational efficiency for CPU and GPU serving workloads.
- Contribute directly to key components across the serving infrastructure — from model container builds and deployment workflows to runtime systems like routing, caching, observability, and intelligent autoscaling — 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.
- Lead technical initiatives that improve latency, availability, and cost-effectiveness across both customer-facing and foundational serving layers.
- Establish best practices for code quality, testing, and operational readiness, and mentor other engineers through design reviews and technical guidance.
What we look for:
- 5+ years of experience building and operating large-scale distributed systems.
- Experience in model serving, inference systems, or related infrastructure (e.g., routing, scheduling, autoscaling, and observability).
- 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.
- Experience building architecture for large-scale, performance-sensitive CPU/GPU inference systems.
- Strong communication skills and ability to collaborate across teams in fast-moving environments.
- Customer-focused mindset with the ability to align implementation details with product goals.
- 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
$166,000—$225,000 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 Twitter, LinkedIn 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.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Scalability
- System Design
- Distributed Systems
- Backend
- Observability
- Machine Learning Infrastructure
- Algorithms
- Routing
- Caching
- GPU
Возможные вопросы на собеседовании
Проверка понимания специфики работы с ИИ-моделями на уровне инфраструктуры.
Как бы вы спроектировали систему динамического пакетного режима (dynamic batching) для минимизации задержек при инференсе LLM?
Оценка навыков проектирования масштабируемых систем.
Опишите архитектуру системы автоскейлинга для GPU-кластеров, учитывая долгое время прогрева (cold start) контейнеров.
Проверка опыта работы с распределенными системами.
Как обеспечить строгую согласованность данных и низкую задержку в системе кэширования ответов модели в разных регионах?
Оценка способности решать проблемы производительности.
С какими узкими местами вы сталкивались при передаче данных между CPU и GPU и как вы их оптимизировали?
Проверка лидерских качеств и менторства.
Расскажите о случае, когда вам пришлось убеждать кросс-функциональную команду принять сложное архитектурное решение, с которым они были не согласны.
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- Страна
- США
- Зарплата
- 166 000 $ – 225 000 $