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Senior ML Engineer – Token Factory
Nebius GroupSenior ML Engineer working on high-performance inference and fine-tuning platform at Nebius Cloud. Driving production speedups and optimising GPU workloads for AI applications.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in optimizing large language model (LLM) performance through advanced inference techniques and low precision training, leveraging strong software engineering skills in Python and modern deep learning frameworks.
Highest-signal resume keywords
Inference OptimizationLow Precision Training & InferenceGPU Workload ProfilingDeep Learning FrameworksSoftware Engineering Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Machine LearningTransformer ArchitecturePython ProgrammingLow Precision TrainingInference Optimization TechniquesProfiling ToolsNeural Network TrainingCI/CDVersion ControlUnit Testing
Soft Skills
Strong CommunicationLeadership Abilities
Tools & Technologies
NsightPyTorch Profiler
Industry Keywords
LLM ArchitecturesGPU Memory HierarchyQuantisationSharding StrategiesCustom Kernels
Tech Stack
Tools & technologiesCloudFlashPythonPyTorch
About the role
Key responsibilities & impact- Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs.
- We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits.
- Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.
- Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups.
- Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
- Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
- Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.
Requirements
What you’ll need- A profound understanding of theoretical foundations of machine learning and transformer architecture.
- Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
- Understanding of GPU memory hierarchy and compute/memory tradeoffs
- Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
- Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
- Strong software engineering skills (we mostly use Python)
- Deep experience with modern deep learning frameworks
- Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
- Strong communication and leadership abilities
Benefits
Comp & 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