Top Machine Learning Jobs in Nagoya

Leads pre-sales technical execution for Fortinet’s AI security solutions across Japan. Responsibilities include technical discovery, architecture reviews, demonstrations, proofs of concept, customer deployments, partner enablement, demo environment management, training, and post-sales handoffs. The role supports AI security adoption, develops relationships with technical decision-makers, and serves as an evangelist for secure AI and agentic AI deployment.

Lead technical onboarding and deployment of the Cybereason/LevelBlue cybersecurity platform. Configure solutions to customer requirements, connect sensors and servers, troubleshoot implementation issues, manage milestones and schedules, and coordinate escalations across Support, DevOps, R&D, Engineering, and Product teams. Build long-term customer relationships, provide product and security training, deliver technical consulting, and maintain regular communication throughout deployment. The role requires extensive technical experience, networking and operating-system expertise, strong project coordination, and fluent Japanese and English communication skills.

Develop agentic AI workflows that automate audio ML experimentation, including data preparation, training, evaluation, hyperparameter searches, benchmarking, regression testing, reporting, and reproducible execution. Build GPU scheduling, experiment tracking, artifact management, and monitoring tools across on-premises and cloud infrastructure. Collaborate with audio researchers to create reliable workflows, dashboards, and reports.

Lead technical onboarding and deployment of the Cybereason cybersecurity platform. Configure solutions to customer requirements, support sensor-server connectivity, troubleshoot technical issues, manage milestones and schedules, coordinate escalations across Support, DevOps, and R&D, and provide product, security, and technical training while maintaining customer partnerships.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Build and deploy engineering-grade digital models for Japanese utility clients by integrating GIS, LiDAR, and other data sources. Configure platform projects, create dashboards, resolve model inaccuracies, support technical delivery and pre-sales, and collaborate with product and engineering teams on platform improvements and Japanese localization. The role requires customer-facing communication in Japanese, global collaboration in English, mentoring, and occasional travel to customers and international offices.

Lead go-to-market strategy and quota-carrying enterprise sales for Fortinet’s AI security solutions in Japan. Build pipeline with sales teams and channel partners, conduct executive presentations, workshops, demonstrations, and strategic account selling, and close complex six- and seven-figure deals. Advise customers on AI governance, guardrails, runtime protection, red-teaming, shadow AI, and secure deployments. Train account managers and partners while engaging C-level security, technology, and AI decision-makers.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Optimizes chemical plant operations, production efficiency, process safety, raw material usage, and product quality. The engineer troubleshoots plant issues, analyzes equipment utilization losses, reviews process KPIs and alarm history, supports corrective and preventive actions, ensures compliance with operating permits, and collaborates with operations teams and technical experts. The role requires strong chemical, mechanical, or materials engineering knowledge plus Japanese and English fluency.

Develop machine learning and modeling-and-simulation solutions for complex manufacturing systems. Build digital twins, surrogate models, and optimization tools using engineering principles, physical data, advanced analytics, and ML. Analyze large datasets, support virtual manufacturing qualification, optimize production and warehouse processes, evaluate build-versus-buy solutions, and translate technical insights into actionable business recommendations while collaborating with R&D scientists, engineers, and technology partners.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Drive customer acquisition and expansion across Japan by owning complex enterprise sales cycles for Nebius’s AI and cloud platform. Responsibilities include territory planning, consultative solution selling, executive relationship management, negotiation and closing, partner collaboration, forecasting, and representing the company at industry events. The role is remote in the Greater Tokyo Area and requires regional travel.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Serve as strategic partner and primary contact for enterprise clients, driving ROI from the fraud/AML SaaS platform. Advise on detection performance, ML-based models, rule engines, and best practices; coordinate with Product and Engineering; deliver business reviews, client education, and actionable feedback to reduce fraud and improve operations.

Serve as primary customer contact for high-value accounts, grow relationships, train and support product use, troubleshoot technical/business issues, escalate as needed, drive account growth with sales, and apply data analytics and AI/ML to deliver insights, automation, and executive-ready outputs.

Create and review realistic, locale-specific robotics and natural sciences tasks in Japanese; apply and adapt scoring rubrics; evaluate and score AI and human responses for accuracy, regulatory alignment, and professional quality; produce gold-standard solutions and expert feedback.