Nebius
Senior ML Engineer (AI Research)
Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.
Publicado4 jul 2026
Países elegibles42 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Senior ML Engineer (AI Research)
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
The role
- 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 role
- Guided search and reinforcement learning for agentic systems
- Reinforcement learning for reasoning models
- Web-scale problem collection for training agents
- Efficient model distillation
The role
- 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
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