AI Jobs in Japan

View detail

Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

View detail

Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

View detail

In this role, the Applications Engineer will work closely with customers to understand their requirements, provide technical expertise and customer support during deployment, and ensure successful integration of Hivemind software. Responsibilities include deploying with customers on site globally with approximately 50% travel to support software integration and development activities, becoming an expert user of the Hivemind enterprise software stack and its autonomy modules, providing technical support and training on Hivemind, developing AI and Autonomy applications using the Shield AI enterprise software development kit, assisting the sales team in pre-sales activities such as demos and conferences, assisting in post-sales deployment and integration of Shield AI software products, developing and maintaining technical documentation and training materials, helping customers debug software and API integration issues, collaborating with the product engineering team to address customer feedback and improve products, and acting as a technical leader across engagements by elevating team performance, driving execution across cross-functional teams, and ensuring successful delivery in complex environments.

View detail

Deploy with customers on site globally (approximately 50% travel) to support software integration and development activities. Become an expert user of the Hivemind enterprise software stack and its various autonomy modules. Provide technical support and training to customers on the use of Hivemind. Develop AI & Autonomy applications using the Shield AI enterprise software development kit. Assist the sales team in pre-sales activities such as demos, conferences, and immersions. Assist in post-sales deployment and integration of Shield AI enterprise software products. Develop and maintain technical documentation and training materials. Help customers debug software and API integration issues. Collaborate with the product engineering team to address customer feedback and improve products.

View detail

Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

View detail

Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers by embedding deeply with their engineering teams to enable them and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content such as examples, guides, patterns, and best practices to the OpenAI Cookbook to aid the broader developer community. Gather high-fidelity product insights from customer deployments and translate these into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their software development lifecycle, technical roadmap, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

View detail

The AI Deployment Engineer will deeply embed with the most strategic platform customers, serving as their technical thought partner in ideating and building novel applications on the OpenAI API. They will proactively guide customers on maximizing business impact from their applications and accelerating their time to value, experiment and prototype solutions with and for customers, and forge and manage relationships with customers’ leadership and stakeholders to ensure successful deployment and scale of applications. The role includes contributing to open-source developer and enterprise resources, scaling the AI Deployment Engineering function by sharing knowledge, codifying best practices, and publishing notebooks to internal and external repositories, and validating, synthesizing, and delivering high-signal feedback to the Product and Research teams. The engineer will use their expertise in programming with Python and Javascript.

View detail

Contributors may design rigorous electrical engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

View detail

Contributors design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

View detail

The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.