Lead Machine Learning Expert - AI Products & Engineering Dep (RMI AI & Data Dep)
Rakuten Crimson House, Japan
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Rakuten Mobile
Job Description:
About Organization
Rakuten Mobile is building the worldās first fully virtualized, cloud-native mobile network, redefining telecom through advanced AI. Join us to innovate at the intersection of mobile technology and AI, delivering unparalleled network performance and customer experience.
Job Duties
We are seeking aĀ Lead Machine Learning ExpertĀ with deep expertise in NLP and large-scale model development to spearhead the creation of aĀ Small Language Model (SLM)Ā specifically tailored to the telecom domain. This is a strategic and hands-on role requiring technical leadership inĀ LLM/SLM development, as well as close collaboration across telecom network engineering and AI teams.
A critical aspect of this role involvesĀ collaborating closely with subject matter expertsĀ inĀ Core Networks, RAN (Radio Access Networks), IPTX, and ACIĀ to deeply understand telecom-specific workflows, configurations, and operational data. This understanding will inform theĀ design and fine-tuning of a Small Language Model (SLM)purpose-built for the mobile network domainācapable of interpreting, reasoning, and generating insights from complex, context-rich telecom environments, fundamentally transforming how we operate and optimize our network.
Lead the design, fine-tuning, and deployment of domain-specific Large and Small Language Models (LLMs/SLMs) for telecom applications.
Collaborate with telecom experts (Core Networks, RAN, IPTX, ACI) to integrate deep domain knowledge into AI models.
Work with Data Scientists and AI Engineers to incorporate telco-specific use cases, feature engineering, and evaluation frameworks.
Develop models capable of processing large-scale telco-native data (logs, topologies, session traces, etc.).
Evaluate, customize, and optimize foundation models (e.g., LLaMA, Falcon, Mistral) using advanced techniques (LoRA, quantization, distillation).
Architect reusable components for prompt engineering, retrieval augmentation (RAG), and agent frameworks.
Contribute to model deployment strategies, efficient inference pipelines, and scalable MLOps practices.
Mentor junior engineers and provide technical leadership across strategic AI initiatives.
Collaborate with leadership to translate business needs into innovative AI solutions for telecom operations.
Leverage expertise in hybrid cloud-native infrastructure and large-scale AI experimentation to drive platform innovation.
Minimum Qualifications
10+ years in machine learning, including 5+ years in LLM/NLP/SLM development.
Extensive experience fine-tuning and evaluating large-scale transformer models for specialized domains.
Deep understanding of LLM architectures, training pipelines, Agentic AI, and evaluation metrics.
Proven experience with open-source model stacks (Hugging Face, DeepSpeed, LangChain, LoRA/PEFT, etc.).
Strong proficiency in Python and ML frameworks (e.g., PyTorch).
Demonstrated ability to collaborate with non-ML experts to integrate domain-specific logic into ML models.
Experience working with data from critical telecom systems (Core Networks, RAN, Transport, etc.).
Proven track record delivering scalable NLP systems and customer-centric ML solutions.
Bachelorās degree (B.E. or equivalent).
Preferred Qualifications
Hands-on experience developing SLMs for vertical or constrained applications.
Familiarity with 4G/5G architecture, protocol layers, and real-time telecom analytics.
Experience with MLOps and deploying models in production at scale.
Contributions to open-source projects or research in the LLM/SLM space.
Experience with data platforms (OnPrem/Hyperscalers) for telecom-grade analytics and integration.
Masterās degree is a strong plus.
Languages:
English (Overall - 3 - Advanced)* Salary range is an estimate based on our AI, ML, Data Science Salary Index š°
Tags: Architecture CX Engineering Feature engineering LangChain LLaMA LLMs LoRA Machine Learning ML models MLOps Model deployment NLP Open Source Pipelines Prompt engineering Python PyTorch RAG Research
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