Senior Machine Learning Engineer (LLMs, Human-Centered AI) – Tokyo / Hybrid | Visa + Salary Insights

📍 Location: jp

🏷 Type: Not specified

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Job Overview

Woven by Toyota is hiring a Senior Machine Learning Engineer specializing in Large Language Models (LLMs) to build next-generation human-centered AI systems for mobility and smart city applications in Tokyo (Hybrid). This role sits within the innovative Woven City ecosystem, where AI, mobility, and social infrastructure converge. The ideal candidate is an experienced ML practitioner with strong production-grade engineering skills, deep NLP expertise, and the ability to adapt models for Japanese language and cultural nuance. You will contribute to autonomous AI systems, agentic workflows, and scalable LLM infrastructure powering real-world mobility transformation.

📅 Job Timeline & Status

This role is in an active recruitment phase, likely receiving global applications due to Woven by Toyota’s strong brand and AI mobility focus. Candidates with strong LLM experience should apply immediately as rolling review hiring increases competition over time. The absence of a fixed deadline indicates continuous intake, but high-caliber candidates may be fast-tracked.

🌍 Work Eligibility & Location

This is a globally open but Japan-centered role requiring strong bilingual capability. The Hybrid structure includes periodic travel to Woven City labs. Candidates outside Japan can apply if they meet visa and Japanese language expectations for technical evaluation and dataset interpretation.

💰 Salary Intelligence

Compensation is highly competitive for Japan’s AI market, especially given Toyota’s strategic investment in autonomous systems and LLM infrastructure. Total compensation likely includes bonuses, housing support, and retirement benefits, making it attractive for experienced ML engineers in global tech.

📊 Role Breakdown

This role focuses on designing and deploying production-grade LLM systems that power human-centric AI experiences across mobility and smart city environments. You will work on LLM fine-tuning, inference optimization, dataset curation, and agentic workflow design tailored for Japanese language understanding and cultural context. A major part of the role involves building scalable ML pipelines from ingestion to deployment, ensuring systems are reliable in real-time environments.

You will collaborate with cross-functional teams in robotics, cloud infrastructure, and autonomous driving systems. Around 30–40% of your time may involve experimental R&D, while 60–70% focuses on engineering production systems. Key technical depth includes PyTorch, HuggingFace Transformers, embedding models, retrieval systems, and distributed inference optimization. You will also evaluate model behavior using real-world datasets and design guardrails for safe human-AI interaction in mobility environments.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly competitive AI systems role requiring deep expertise in both research and production engineering. Candidates are expected to operate at senior or staff-level capability, particularly in LLM optimization and system architecture.

🚀 Career Impact

This role provides exposure to one of the world’s most ambitious AI mobility projects. Engineers gain experience building real-world LLM systems tied to physical environments, a rare blend of robotics, AI, and smart infrastructure.

📋 Key Responsibilities

You will design and implement LLM-based AI systems that support human interaction in mobility environments. Responsibilities include building scalable ML pipelines, curating and processing datasets for model training, and developing agentic AI workflows that enable autonomous decision-making. You will optimize inference performance using distributed computing and model compression techniques.

Additionally, you will collaborate with researchers and engineers to evaluate model performance in real-world scenarios, especially in Japanese linguistic and cultural contexts. You will also contribute to architecture decisions for AI infrastructure supporting Woven City’s digital ecosystem.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

This role shows high hiring urgency with rolling review and global applicant intake. Competition is expected to be very high due to Toyota’s brand strength and the growing demand for LLM engineers in mobility AI. Interview cycles are likely moderately fast (2–4 weeks), but senior candidates may receive expedited screening. Since no fixed deadline is published, applications may close without notice.