Senior ML Engineer (Token Factory)

Nebiussenior

In short

→Ingeniero ML senior que construye plataformas de entrenamiento e inferencia a gran escala en GPU.
→Día a día: optimiza modelos LLM, prueba métodos de fine-tuning y mejora el rendimiento con JAX y arquitecturas avanzadas.
→Lo destacado: trabajas en una de las mayores infraestructuras de GPU del mundo, con acceso a modelos de vanguardia y hardware de última generación.

Proficiency in English is required

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What they ask for

✓Entendimiento profundo de ML y RL teóricos
✓Experiencia en entrenamiento de modelos grandes en múltiples nodos
✓Dominio de frameworks modernos como JAX
✓Habilidades sólidas en ingeniería de software (Python)
✓Conocimiento de estrategias de sharding y optimización de hardware
✓Capacidad de comunicación y liderazgo efectivo

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

JAXPythonCI/CDversion controlunit testingGPU orchestrationinference optimizationLoRAfull-parameter fine-tuningFP8

Who should you write to at Nebius?

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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 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 an inference & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale. Some directions we currently working on and which you can be a part of: • Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency. • Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation. • Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware 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 • Experience with training large models on multiple computational nodes • Reasonable 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 (we use JAX) • Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing • Strong communication and leadership abilities Nice to have: <ul class="ak-ul" data-inden

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