- Страна
- США
- Зарплата
- 192 000 $ – 260 000 $
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Staff Software Engineer, Model Serving
Исключительная позиция в компании-лидере индустрии AI с конкурентной зарплатой и возможностью влиять на глобальные стандарты инфраструктуры данных. Высокий уровень ответственности и работа с передовыми технологиями (LLM, GPU serving).
Сложность вакансии
Роль уровня Staff требует более 10 лет опыта и глубокой экспертизы в распределенных системах и инференсе моделей. Высокая сложность обусловлена необходимостью принимать архитектурные решения для GPU-нагрузок и менторства опытных инженеров.
Анализ зарплаты
Предложенный диапазон $192k–$260k полностью соответствует рыночным стандартам для позиции Staff Engineer в Сан-Франциско. С учетом бонусов и опционов (equity), совокупный доход может значительно превышать верхнюю границу базового оклада.
Сопроводительное письмо
I am writing to express my strong interest in the Staff Software Engineer position for Model Serving at Databricks. With over a decade of experience in building large-scale distributed systems and a deep focus on high-throughput, low-latency inference, I have closely followed Databricks' leadership in the Data Intelligence space. My background in optimizing GPU/CPU workloads and designing intelligent autoscaling systems aligns perfectly with your mission to provide a unified, governed platform for AI deployment.
In my previous roles, I have led technical initiatives that significantly reduced inference latency and improved operational efficiency for complex ML models. I am particularly drawn to this role because it combines deep technical challenges in system design with the opportunity to influence the broader AI platform strategy. I am eager to bring my expertise in routing, caching, and observability to the Model Serving team and help Databricks' customers operationalize their models at scale.
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Откликнитесь в databricks уже сейчас
Присоединяйтесь к команде Databricks, чтобы определять будущее AI-инфраструктуры и масштабировать системы обслуживания моделей 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 Staff 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.
- 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 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.
- 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.
- Deep expertise in model serving, inference systems, and 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 leading architecture for large-scale, performance-sensitive CPU/GPU inference systems.
- 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 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.
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Навыки
- Apache Spark
- System Design
- Distributed Systems
- Observability
- MLflow
- Data Structures
- Algorithms
- Routing
- Caching
- GPU
- Inference
- Model Serving
- Autoscaling
Возможные вопросы на собеседовании
Проверка опыта проектирования систем с жесткими требованиями к задержкам.
Как бы вы спроектировали систему маршрутизации для Model Serving, чтобы минимизировать задержки при резких скачках трафика?
Оценка понимания специфики работы с GPU в контексте инференса.
Какие основные проблемы возникают при планировании (scheduling) GPU-нагрузок для LLM и как их можно оптимизировать?
Проверка навыков оптимизации затрат и ресурсов.
Опишите ваш подход к реализации интеллектуального автоскейлинга для моделей с разным профилем потребления ресурсов.
Оценка лидерских качеств и умения разрешать технические конфликты.
Расскажите о случае, когда вам пришлось принимать сложное архитектурное решение, с которым была не согласна часть команды. Как вы аргументировали свою позицию?
Проверка понимания жизненного цикла ML-моделей.
Как обеспечить наблюдаемость (observability) и отслеживание происхождения (lineage) моделей в распределенной среде исполнения без значительного влияния на производительность?
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- Страна
- США
- Зарплата
- 192 000 $ – 260 000 $