高级机器学习工程师（AI研究）
Senior ML Engineer (AI Research)
Nebius简介：
Nebius正在引领全球人工智能经济的云基础设施新时代。我们正在构建一个全栈式AI云平台，支持开发者和企业从数据和模型训练到生产部署的全流程，而无需承担自建大型内部AI/ML基础设施的成本和复杂性。
由工程师打造，面向工程师。从大规模GPU编排到推理优化，我们在计算、存储、网络和应用AI领域都负责解决难题。
在纳斯达克上市（NBIS），总部位于阿姆斯特丹，我们在欧洲、英国、北美和以色列设有研发中心，拥有全球业务布局。我们的团队超过1500人，包括数百名在硬件、软件和AI研发方面有深厚专业知识的工程师。
职位描述
该职位属于Nebius AI R&D团队，专注于AI的应用研究。我们近期发表的应用研究成果包括：
- 在长上下文多轮对话场景中应用强化学习进行智能体训练
- 大幅扩展任务数据收集以支持SWE智能体的强化学习
- 构建定期更新的SWE智能体去污染评估
- 研究如何利用测试时引导搜索来构建更强大的智能体
这些成果通常会与相邻团队合作，将我们的研究成果应用于实际场景。
我们目前正寻找高级和资深级别的机器学习工程师，从事以下领域的研究：
- 智能系统中的引导搜索和强化学习
- 推理模型的强化学习
- 用于智能体训练的网络规模问题收集
- 高效的模型蒸馏
你可能需要负责的工作包括：
- 进行实验，找出在不同环境交互痕迹上高效训练大语言模型的方法
- 探索轨迹空间中的引导生成和搜索方法
- 提出在网页规模上挖掘相关数据的方法，并找到高效利用这些数据进行模型后训练的方式
- 在可验证领域中对不同的强化学习配置进行实验
- 探索在无验证奖励信号的任务上训练AI智能体的方法
我们期望你具备：
- 对机器学习和强化学习理论基础的深刻理解
- 在现代语言处理和生成的深度学习方面的专业知识
- 在深度学习领域有丰富的经验
查看英文原文
About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
The role
This role is for Nebius AI R&D, a team focused on applied research in AI. Examples of applied research that we have recently published include:
- applying reinforcement learning for agent training in long-context multi-turn scenarios
- dramatically scaling task data collection to power reinforcement learning for SWE agents
- building a decontaminated evaluation for SWE agents that is regularly updated
- investigating how test-time guided search can be used to build more powerful agents
The results often lead to collaboration with adjacent teams where our research findings are applied in practice.
We are currently looking for senior- and staff-level ML engineers to work on research in areas such as:
- Guided search and reinforcement learning for agentic systems
- Reinforcement learning for reasoning models
- Web-scale problem collection for training agents
- Efficient model distillation
Some examples of what your responsibilities might include are:
- Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments
- Exploring methods of guided generation and search in the trajectory space
- Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training
- Conducting experiments with different reinforcement learning configurations in verifiable domains
- Exploring methods to train AI agents on tasks with non-verifiable reward signals
We expect you to have:
- A profound understanding of theoretical foundations of machine learning and reinforcement learning
- Deep expertise in modern deep learning for language processing and generation
- Substantial experience with training large models on multiple computational nodes
- Strong software engineering skills (we mostly use python)
- Deep experience with modern deep learning frameworks (we use jax)
- Strong communication and leadership abilities
- Experience designing, executing, and analyzing machine learning experiments with proper statistical rigor
- Ability to formulate research questions, design experiments to test hypotheses, and draw meaningful conclusions from results
- Ability to document research findings clearly and contribute to technical publications or report
Nice to have:
- Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, PPO etc
- Familiarity with important ideas in LLM space, such as RoPE, ZeRO/FSDP, Flash Attention, quantization
- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Master’s or PhD preferred
- Track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment
- Experience in engineering complex systems, such as large distributed data processing systems or high-load web services
- Open-source projects that showcase your engineering prowess
- Excellent command of the English language, alongside superior writing, articulation, and communication skills
- Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.