训练后研究工程师
Post-Training Research Engineer
ABOUT BASETEN
Baseten 为全球最具活力的 AI 公司提供关键推理支持，例如 Cursor、Notion、OpenEvidence、Abridge、Clay、Gamma 和 Writer。通过结合应用 AI 研究、灵活的基础架构和无缝的开发者工具，我们使处于 AI 前沿的公司能够将前沿模型投入生产。我们正在快速成长，并最近完成了 15 亿美元 F 轮融资 https://www.baseten.co/blog/announcing-our-series-f/，由 Altimeter Capital、Conviction Partners 和 Spark Capital 领投。加入我们，帮助构建工程师们用来交付 AI 产品的平台。
我们正在寻找一位在机器学习方面有丰富经验，并具备扎实数学和计算机科学基础的工程师，加入 Baseten 的 Post-Training 团队。
THE ROLE
定制模型对 Baseten 客户的成功至关重要。从推理流量来看，Baseten 绝大多数流量都是与经过后训练的模型进行的，无论是通过强化学习、监督微调、文献中的最新技术，还是 Baseten 内部研究技术。Post-Training 团队负责客户后训练模型的成功，我们采用多种技术来生成比甚至最大闭源模型更高效、质量更高的模型，以满足客户的特定需求。
你的角色是构建内部工具链来支持这一切。我们关注如何高效且大规模地训练各种不同的模型架构和多种技术。有时这需要深入研究某个特定的技术主题，但更多时候需要整体跨堆栈工作——包括系统级概念如 Kubernetes、cgroups、存储系统和网络拓扑，以及 PyTorch 分布式张量计算和 GPU 内核。
The Manifesto: https://labs.baseten.co/manifesto
RECENT RESEARCH
- 密集、策略或两者都有？https://www.baseten.co/research/dense-on-policy-or-both/
- 长期运行代理的重复 KV 缓存 https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/
- 没有黑暗的蒸馏——在 Baseten 上复制黑盒策略蒸馏 https://www.baseten.co/research/distillation-without-the-dark/
我们没有一套固定的技能要求，但以下是我们正在寻找的一些内容：
- 对...有深入理解
查看英文原文
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
We are looking for an engineer with strong experience in machine learning and solid foundations in maths and computer science to join our growing Post-Training team at Baseten.
THE ROLE
Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs.
Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often if involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels.
The Manifesto: https://labs.baseten.co/manifesto
RECENT RESEARCH
- Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/
- Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/
- Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/
We don’t have a rigid set of skills, but here’s some of what we’re looking for:
- A deep understanding of modern ML techniques and tools for training transformers
- Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar
- A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism
- The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library
- The ability to perform roofline analysis on a transformer training setup
- A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute
- Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask
- Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect
- Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerisation, and networking protocols
- A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices
BENEFITS
- Competitive compensation, including meaningful equity
- (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
- Paid parental leave
- Fertility and family-building stipend through Carrot
- Company-facilitated 401(k)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).