Senior ML Engineer (AI Research)
Salary not listed • Remote • Amsterdam, Netherlands; Israel; Remote - Europe; United Kingdom • Full-time
Posted 1y ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Amsterdam, Netherlands; Israel; Remote - Europe; United Kingdom
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Research Engineer
About the job
Nebius is seeking senior- and staff-level ML Engineers to join its AI R&D team, focusing on applied research in AI. This team publishes research in areas such as applying reinforcement learning for agent training, scaling data collection for reinforcement learning, and developing robust evaluation methods for AI agents. The role involves conducting experiments to discover efficient methods for training large language models, exploring guided generation techniques, mining web-scale data for model post-training, and experimenting with reinforcement learning configurations. Research findings often lead to practical applications through collaboration with other teams.
Responsibilities
- Conduct experiments to find efficient ways to train large language models on interaction traces.
- Explore methods for guided generation and search in trajectory spaces.
- Develop strategies for mining relevant data at web scale and using it for model post-training.
- Conduct experiments with different reinforcement learning configurations in verifiable domains.
- Explore methods to train AI agents on tasks with non-verifiable reward signals.
Requirements
- Profound understanding of theoretical foundations of machine learning and reinforcement learning.
- Deep expertise in modern deep learning for language processing and generation.
- Substantial experience training large models on multiple computational nodes.
- Strong software engineering skills, primarily in Python.
- Deep experience with modern deep learning frameworks, specifically JAX.
- Strong communication and leadership abilities.
- Experience designing, executing, and analyzing machine learning experiments with statistical rigor.
- Ability to formulate research questions, design experiments, and draw conclusions.
- Ability to document research findings and contribute to technical publications.
- Experience with deep reinforcement learning for LLMs, including reward modeling, DPO, PPO.
- Familiarity with LLM concepts like RoPE, ZeRO/FSDP, Flash Attention, and quantization.
- Experience engineering complex systems like large distributed data processing or high-load web services.
- Proficiency in contemporary software engineering approaches (CI/CD, version control, unit testing).
Benefits
- 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