首席AI研究科学家，研究总监 - AI扩展
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Principal AI Research Scientist, Research Director - AI ScalingDatabricks · Mountain View, California; San Francisco, California
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分数构成
- 时效性 （随职位发布时间变化）+18
- 雇主官方来源+15
- 稀有职位+5
- 公司来源健康度+8
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职位描述
机器翻译首席AI研究科学家，研究总监 - AI扩展
P-1227
关于Databricks AI
在Databricks，我们致力于让数据团队能够解决世界上最棘手的问题，从安全威胁检测到癌症药物研发，通过构建和运行世界上最好的数据和AI平台。Databricks AI研究组织使企业能够使用自己的数据开发AI模型和智能体，技术范围从后训练开源LLM到开发先进的多智能体架构。Databricks AI坚信，公司的AI模型和智能体与任何其他核心知识产权一样宝贵，高质量的AI应该惠及所有人。
关于扩展研究团队
Databricks AI扩展团队专注于将大语言模型（LLM）训练和推理效率的边界推向超出支持现有模型所需水平。该团队探索跨算法、系统和基础设施的扩展与效率改进的新途径，需要既能推动独立研究议程，又能与工程合作伙伴深入低层实现细节的研究人员。
职位概述
作为首席研究科学家 – AI扩展，您将领导一支由世界级研究人员和工程师组成的团队，推进大规模机器学习的最先进水平，重点关注后训练、RL和推理效率、优化以及扩展。您将定义并执行研究路线图，推进Databricks AI平台，并在客户大规模训练、服务和适配LLM的方式上带来切实改进，与产品、数据和工程负责人紧密合作，将前沿方法投入生产。
您将产生的影响
• 领导并发展一支专注于基础和应用AI问题的多学科研究团队，特别强调LLM扩展、效率和系统性能。
• 根据Databricks的战略目标定义扩展研究路线图，优先推进基础模型效率以及大规模训练和推理方面的进展。
• 推动大规模神经网络训练和推理的算法创新，包括新型优化器、低精度技术和模型适配方法，并指导您的团队针对最先进方法进行严格的实证验证。
• 通过与核心系统和平台团队紧密合作，优化用于分布式训练和RL、内存效率以及计算效率的端到端ML系统，确保研究想法转化为高性能、可靠的基础设施。
• 与产品和工程部门合作，将研究突破，尤其是围绕扩展和效率的突破，转化为Databricks AI平台中影响客户的能力。
• 培养科学卓越和开放的文化，包括高质量研究实践、可复现实验，以及在整个Databricks AI内进行有效的内部知识共享。
• 通过顶级出版物、会议演讲以及与学术界和开源社区的合作，在外部代表Databricks AI研究，重点关注大规模模型的优化和效率。
• 指导并培养人才，为研究科学家和工程师提供技术指导（研究议程、实验、实现）和职业发展支持。
岗位职责
• 定义并领导关于基础模型效率的独立研究项目，涵盖优化器设计、低精度训练/推理、可扩展模型架构和高效适配方法等主题。
• 监督大规模实验的设计和执行，包括针对最先进方法的基准测试，以及评估质量、延迟、吞吐量和成本之间的权衡。
• 与您的团队亲自动手编写高质量、高效的Python和PyTorch代码，用于研究实现、快速原型开发以及与Databricks生产系统的集成。
• 与分布式系统和基础设施团队合作，推动LLM和其他大型模型在分布式训练、并行策略、内存管理和硬件利用率方面的极限。
• 建立以扩展为重点的研究指标、评估协议和最佳实践（例如训练效率、推理成本、能耗），并推动其在Databricks AI内的采用。
• 倡导以负责任且稳健的方式部署扩展创新，确保模型行为、可靠性和安全性始终是一等考虑因素。
我们寻找的人才
• 具备领导研究团队开发基础模型效率及相关主题新技术的可靠能力，并有强大的行业影响力记录。
• 在以下至少一个领域拥有深厚专业知识：生成式AI、LLM、分布式ML系统、模型优化或负责任AI，并高度重视大规模神经网络的扩展和效率。
• 亲自动手的领导力 - 强大的编程技能，并展示出能够编写高质量、高效的Python和PyTorch代码以进行研究实现和实验的能力。
• 展示出与产品和工程团队合作，将研究创新转化为可扩展产品能力的能力。
• 出色的沟通、领导和利益相关者管理技能，具有影响跨职能路线图并使研究与业务影响保持一致的经验。
加分项
• 曾在系统与ML交叉领域工作，例如分布式训练框架、深度学习工作负载的编译器和内核优化，或内存/计算高效模型设计。
• 在大规模ML领域拥有强大的行业和学术网络，并在ML和系统顶级会议中持续合作或服务（例如PC/领域主席）。
• 强大的研究影响力记录——例如在顶级ML/系统会议（如ICLR、ICML、NeurIPS、MLSys）上发表的第一作者论文、有影响力的开源贡献或广泛使用的已部署系统——尤其是在优化或效率方面。
福利待遇
在Databricks，我们致力于提供满足所有员工需求的全面福利和津贴。有关您所在地区所提供福利的具体详情，请点击此处。
我们对多元化和包容性的承诺
在Databricks，我们致力于营造多元和包容的文化，让每个人都能脱颖而出。我们非常重视确保我们的招聘实践具有包容性，并符合平等就业机会标准。在Databricks寻求就业的个人不会因年龄、肤色、残疾、族裔、家庭或婚姻状况、性别认同或表达、语言、国籍、身体和心理能力、政治派别、种族、宗教、性取向、社会经济状况、退伍军人身份以及其他受保护特征而受到区别对待。
合规
如果履行工作职责需要访问出口管制技术或源代码，雇主可自行决定是否为此类职位申请美国政府许可证，且雇主可能仅基于此原因拒绝继续推进申请人。
薪资
Databricks致力于公平和公正的薪酬实践。该职位的薪资范围列于下方，代表非佣金制职位的预期薪资范围或佣金制职位的目标收入。实际薪酬方案基于每位候选人独有的若干因素，包括但不限于与工作相关的技能、经验深度、相关认证和培训以及具体工作地点。基于上述因素，Databricks预计将使用该范围的全部宽度。该职位的总薪酬方案还可能包括年度绩效奖金、股权以及上述福利的资格。有关您所在地区属于哪个范围的更多信息，请访问我们的页面此处。
当地薪资范围 $270,000 — $340,000 USD
关于Databricks
Databricks是数据和AI公司。全球超过20,000家组织——包括adidas、AT&T、Bayer、Block、Mastercard、Rivian、Unilever以及70%的财富500强——依赖Databricks Data + AI Platform来构建和扩展数据与AI应用、分析和智能体。Databricks总部位于旧金山，在全球拥有30多个办事处，提供统一平台，包括Genie、Lakebase、Agent Bricks、Lakeflow、Lakehouse和Unity Catalog。如需了解更多信息，请在LinkedIn、X、YouTube和Instagram上关注Databricks。
以上内容由机器翻译自动生成，可能存在错误；投递前请以雇主原文为准。
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职位描述
Principal AI Research Scientist, Research Director - AI Scaling
P-1227
About Databricks AI
At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development, by building and running the world’s best data and AI platform. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.
About the Scaling Research Team
The Databricks AI Scaling team focuses on pushing the boundaries of large language model (LLM) training and inference efficiency beyond what is required to support existing models. The team explores novel avenues for scaling and efficiency improvements across algorithms, systems, and infrastructure, requiring researchers who can both drive independent research agendas and dive deep into low‑level implementation details with engineering partners.
Role Summary
As a Principal Research Scientist – AI Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting‑edge methods into production.
The Impact You Will Have
• Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a particular emphasis on LLM scaling, efficiency, and systems performance.
• Define the scaling research roadmap in alignment with Databricks’ strategic objectives, prioritizing advances in foundation model efficiency and large‑scale training and inference.
• Drive algorithmic innovations for large‑scale neural network training and inference, including novel optimizers, low‑precision techniques, and model adaptation methods, and guide your team in rigorous empirical validation against state‑of‑the‑art approaches.
• Optimize end‑to‑end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure.
• Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into customer‑impacting capabilities in the Databricks AI platform.
• Foster a culture of scientific excellence and openness, including high‑quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI.
• Represent Databricks AI research externally through top‑tier publications, conference talks, and collaborations with academia and the open‑source community, with a focus on optimization and efficiency for large‑scale models.
• Mentor and develop talent, providing both technical guidance (research agendas, experimentation, implementation) and career development support for research scientists and engineers.
岗位职责
• Define and lead independent research programs on foundation model efficiency, covering topics such as optimizer design, low‑precision training/inference, scalable model architectures, and efficient adaptation methods.
• Oversee the design and execution of large‑scale experiments, including benchmarking against state‑of‑the‑art methods and evaluating trade‑offs in quality, latency, throughput, and cost.
• Work hands‑on with your team on high‑quality, efficient code in Python and PyTorch for research implementation, rapid prototyping, and integration with Databricks’ production systems.
• Collaborate with distributed systems and infra teams to push the limits of distributed training , parallelism strategies, memory management, and hardware utilization for LLMs and other large models.
• Establish metrics, evaluation protocols, and best practices for scaling‑focused research (e.g., training efficiency, inference cost, energy usage) and drive their adoption across Databricks AI.
• Champion responsible and robust deployment of scaling innovations, ensuring that model behavior, reliability, and safety remain first‑class considerations.
What We Look For
• Proven ability to lead a research team to develop novel techniques for foundation model efficiency and related topics, with a strong track record of industry impact.
• Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with a strong emphasis on scaling and efficiency for large‑scale neural networks.
• Hands on leadership - strong programming skills and demonstrated ability to write high‑quality, efficient code in Python and PyTorch for research implementation and experimentation.
• Demonstrated ability to translate research innovation into scalable product capabilities in partnership with product and engineering teams.
• Excellent communication, leadership, and stakeholder management skills, with experience influencing cross‑functional roadmaps and aligning research with business impact.
Nice to Have
• Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler and kernel optimization for deep learning workloads, or memory‑/compute‑efficient model design.
• Strong industry and academic network in large‑scale ML, with ongoing collaborations or service (e.g., PC/area chair) at top conferences in ML and systems.
• A strong record of research impact—such as first‑author publications at top ML/systems conferences (e.g., ICLR, ICML, NeurIPS, MLSys), influential open‑source contributions, or widely used deployed systems—especially in optimization or efficiency.
福利待遇
At 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 click here .
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.
薪资
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 $270,000 — $340,000 USD
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram .