Top Artificial Intelligence Jobs in Yokohama

Lead verification efforts for DRAM and emerging memory designs by developing testcases, testbenches, and verification environments. Simulate, analyze and debug pre-silicon full-chip designs using Spice or Verilog flows, run regressions for coverage, and collaborate internationally to evolve verification methodology and flows.

Lead international commercial build for data, engineering and AI: own data foundation, shared engineering pool, solution architecture, AgentOps, AI evaluation, model routing and CIO platform integration to deliver production-grade, governed AI and data products.

Mentor students in Nanodegree programs, provide project reviews and technical support, offering personalized feedback and responding to queries promptly.

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Early-career software engineer in a fixed-term accelerator contributing to backend/full-stack feature development, testing, CI/CD, and deployments. Collaborate on API and system design, integrate and deploy AI/LLM capabilities (LLM APIs, RAG, embeddings, agents), build datasets/benchmarks, monitor performance, debug issues, and produce technical documentation.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Design, build, and maintain high-performance backend services and APIs using Java/Spring Boot. Develop and productionize recommendation algorithms and LLM systems, optimize LLM workloads (including on-prem), ensure scalability, reliability, and security, and collaborate across time zones with product and project teams to own end-to-end architecture.

Lead planning, design, delivery and promotion of a customer-service chatbot management platform. Use AI and big data to improve CS operations (corpus training, SOPs, assistant replies, summaries). Collaborate with engineering, UX, and CS teams. Monitor KPIs and user cases to drive product improvements.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Responsible for functional and API testing of platform products, designing and executing end-to-end tests across frontend, backend, strategy, and algorithms. Validate personalized user experiences, improve test coverage and QA processes, and drive adoption of AI-assisted test generation and automation. Collaborate with Product, Engineering, Algorithm, and Strategy teams to identify issues and ensure quality.

Lead AI-driven growth product strategy to improve acquisition, activation, retention, cross-sell, and churn reduction. Design viral loops and referral programs, use data and user insights to inform features, manage roadmaps, run experiments, and collaborate with marketing, operations, and engineering to optimize user funnels and drive scalable organic growth.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Build and optimize high-traffic, responsive web applications and hybrid in-app pages. Implement user-facing features, improve performance and scalability, and create libraries/frameworks to accelerate development and enhance code quality.

Early-career full-time program offering training, mentorship, and hands-on work within Binance's blockchain ecosystem. Participants develop practical skills in Web3, collaborate across teams, and contribute to product and engineering projects while receiving tailored curriculum and 1:1 mentorship.

Use your smartphone to converse with AI chatbots in Vietnamese (voice mode) following predefined prompts and goals, then rate each interaction in English on your laptop. Complete onboarding, follow confidentiality and detailed guidelines, and use personal devices (smartphone and laptop) to perform project-based tasks with variable hours.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Develop and maintain scalable backend services and APIs, collaborate with frontend developers, and lead new features and infrastructure improvements.

Provide technical support and own resolution of customer issues for Azure Database for MySQL and PostgreSQL; handle high-severity incidents; participate in readiness, knowledge sharing, and product/process improvements; provide Korean case-queue coverage on a 14x7 shift schedule.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.