ML Engineer, Large Language Models (LLM Training & Inference Optimization)

The role

This role is for Nebius AI R&D, a team focused on applied research and the development of AI-heavy products. Examples of applied research that we have recently published include:

• investigating how test-time guided search can be used to build more powerful agents;
• dramatically scaling task data collection to power reinforcement learning for SWE agents;
• maximizing efficiency of LLM training on agentic trajectories.

One example of an AI product that we are deeply involved in is Nebius AI Studio — an inference and fine-tuning platform for AI models.

We are currently in search of senior and staff-level ML engineers to work on optimizing training and inference performance in a large-scale multi-GPU multi-node setups. 

This role will require expertise in distributed systems and high-performance computing to build, optimize, and maintain robust pipelines for training and inference.

Your responsibilities will include: 

• Architecting and implementing distributed training and inference pipelines that leverage techniques such as data, tensor, context, expert (MoE) and pipeline parallelism. 
• Implementing various inference optimization techniques, e.g. speculative decoding and its extensions (Medusa, EAGLE, etc.), CUDA-graphs, compile-based optimization.
• Implementing custom CUDA/Triton kernels for performance-critical neural network layers.

We expect you to have:

• A profound understanding of theoretical foundations of machine learning
• Deep understanding of performance aspects of large neural networks training and inference (data/tensor/context/expert parallelism, offloading, custom kernels, hardware features, attention optimizations, dynamic batching etc.)
• Expertise in at least one of those fields:
• Implementing custom efficient GPU kernels in CUDA and/or Triton
• Training large models on multiple nodes and implementing various parallelism techniques
• Inference optimization techniques - disaggregated prefill/decode, paged attention, continuous batching, speculative decoding, etc.
• Strong software engineering skills (we mostly use python)
• Deep experience with modern deep learning frameworks (we use JAX & PyTorch)
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
• Strong communication and ability to work independently

Nice to have:

• Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT-LLM, Dynamo)
• Familiarity with important ideas in LLM space, such as MHA, 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

What we offer 

• Competitive salary and comprehensive benefits package.
• Opportunities for professional growth within Nebius.
• Hybrid working arrangements.
• A dynamic and collaborative work environment that values initiative and innovation.

We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!