【JAPAN AI】Research Engineer, LLM modeling / English
株式会社ジーニー
給与：1200万円〜2000万円
雇用形態：正社員
勤務地：東京都
仕事内容
・Agent Research & Development
‐Conceive, develop, and compare different agent harnesses (memory, context compression, inter-agent communication architectures, etc.)
‐Research and develop new reasoning, planning, and retrieval methods
‐Develop technologies for multimodal and long-context handling
‐Survey, reproduce, and improve upon the latest research papers
・Evaluation & Benchmarking
‐Design and implement rigorous quantitative benchmarks for large-scale agentic tasks
‐Design synthetic data generation and evaluation benchmarks
‐Support automated evaluation of models and prompts (across the full lifecycle from training to production)
・Production Problem-Solving
‐Optimize inference latency and cost (quantization, distillation, caching, etc.)
‐Create and optimize training data mixes
‐Advance agent evaluation frameworks
‐Improve quality and tune performance in production environments
・Knowledge Transfer & Outreach
‐Transfer technology and mentor the Agentic Product Engineer team
‐Collaborate with academic institutions and OSS communities
「Key Results (KR/Metrics)」
・Benchmark score improvement rate (internal and public benchmarks)
・Number of novel methods shipped to production (per quarter)
・Inference latency and cost reduction rate
・Number of papers and technical blog posts published
・Number of internal knowledge transfers completed
「Tech Stack」
・Languages: Python (research / framework), TypeScript / React / Next.js (frontend) / NX
・ML/AI: PyTorch, JAX, Transformers, vLLM, Weights & Biases
・Infrastructure: GCP (containers / K8s), Docker
・Tools: Slack, Confluence, Linear, Google Workspace, GitHub, Notion
・AI Dev Support: Claude Code MAX Plan, Cursor, ChatGPT, Devin
・Hardware: Mac (Apple Silicon), dual monitors
応募資格
・Master's or Ph.D. in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields
・Experience developing complex agentic systems using LLMs
・Significant hands-on experience in software engineering and ML
・Experience with LLM prompt engineering and/or building products with language models
Experience with large-scale model training and inference using PyTorch or JAX
・Deep understanding of LLM and Transformer architectures
・Ability to read, reproduce, and improve upon research papers
・Strong implementation skills in Python (production-quality code)
・Language requirement (at least one of the following):
‐Japanese: Fluent — able to discuss product development without friction
‐English: Business level