Senior AI Backend Engineer

Key Responsibilities

Design, implement, and maintain robust AI‑driven backend services for autonomous truck operation.
Build and evolve cloud‑native platform frameworks for scalable data ingestion, storage, and processing.
Architect and optimize distributed system components, including task orchestration, fault handling, and high‑throughput service interfaces.
Develop fast‑retrieval pipelines for large‑scale data, ensuring low latency and high reliability.
Manage metadata, model‑training preparation, and multi‑modal data transformations to support AI model lifecycle.
Implement observability, production debugging, and resource‑optimization strategies to keep services performant.
Design clean, versioned Service APIs and structured storage solutions for internal and external partners.
Lead troubleshooting and root‑cause analysis for production incidents, driving continuous improvement.
Collaborate across AI research, vehicle control, and product teams to translate research breakthroughs into production‑ready services.
Mentor junior engineers and champion best practices in software engineering, security, and scalability.

Bachelor’s degree or higher in Computer Science, Electrical Engineering, or a related field.
2–5 years of professional experience building and operating backend systems at scale.
Strong mastery of AI infrastructure concepts and experience with large‑scale data pipelines.
Proficiency in cloud‑native platforms (Kubernetes, Docker, Helm) and modern backend frameworks.
Hands‑on experience with distributed system design, task orchestration (e.g., Airflow, Celery), and fault‑tolerant architectures.
Deep understanding of high‑throughput service APIs, fast data retrieval techniques, and structured storage (SQL/NoSQL).
Demonstrated ability to implement observability (metrics, tracing, logging) and production debugging tools.
Excellent problem‑solving skills, with a track record of troubleshooting complex, real‑time systems.
Strong communication skills in English; Japanese language ability is a plus.

Experience with AI model training pipelines, metadata management, and multi‑modal data transformations.
Familiarity with resource optimization (autoscaling, cost‑aware scheduling) in cloud environments.
Background in autonomous vehicle technology, robotics, or related deep‑tech domains.
Knowledge of safety‑critical system requirements and regulatory compliance for automotive software.
Ability to work in a fast‑moving startup environment and drive initiatives from concept to production.