No Japanese Required Software Engineer, AI Platform

Role & Expectations

As a Software Engineer (AI Platform), you will power the reliability, performance, and cost efficiency of the entire AI platform through backend engineering.

• Design, implement, and operate backend services while also optimizing Kubernetes clusters and cloud infrastructure
• Design and build observability infrastructure (tracing, logging, metrics) to rapidly detect and resolve failures unique to AI agents
• Deliver improvements with direct business impact through inference cost and infrastructure cost optimization
• Maintain 99.9% uptime through SLI/SLO design and operations, on-call, and incident response
• Improve developer experience for in-house engineers through CI/CD pipeline construction and development environment improvements

Why You'll Love This Role

• At the intersection of Backend × Infrastructure — A new domain where you support the entire platform through the power of backend engineering.
• Platform engineering for the AI era — Go beyond traditional infrastructure / SRE to tackle AI-specific challenges: inference cost optimization, GPU management, agent tracing, and more.
• Large-scale cloud infrastructure design — Gain experience designing and operating large-scale distributed systems with Kubernetes, event-driven architectures, and autoscaling.
• Cost optimization with real impact — Inference and infrastructure cost optimization directly translates to business impact. Improving $/request ripples across all products.
• Powering every product — Support 99.9% uptime for a production environment used by ~200 companies. Every AI agent runs on the infrastructure you build.
• Rapid-growth environment — In a startup that has grown to 200+ people and 9 products in just 3 years, you will have significant autonomy in technical decision-making.

Job Description

• Backend Services & Platform Development
  • Design, implement, and operate backend services for the AI platform
  • Design, build, and operate Kubernetes clusters
  • Architect and optimize cloud infrastructure (GCP)
  • Codify and automate infrastructure with IaC (Terraform)
  • Cost/performance optimization (autoscaling, caching, batch processing, GPU management)
• Observability & Governance
  • Design and build the observability stack (tracing, logging, metrics)
  • Implement AI agent-specific tracing (inference request tracking, tool call visualization)
  • Build data access and permission management infrastructure
  • Address security requirements
• SRE & Reliability
  • Maintain platform uptime of ≥99.9%
  • Design and operate SLIs / SLOs
  • On-call, incident response, and post-mortems
  • Continuously improve incident MTTR
• Developer Experience
  • Build and improve CI/CD pipelines
  • Maintain development and staging environments
  • Create and maintain infrastructure documentation for internal engineers

Work Style

Hybrid work model: 3 days in the office, 2 days remote.

Minimum Qualifications

• Bachelor's degree or equivalent practical experience in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields
• 3+ years of practical experience as a backend engineer
• Production product development experience in Python
• Design and operations experience on cloud platforms (AWS / GCP / Azure)
• Understanding and operational experience with Kubernetes / container orchestration
• Distributed system design and operations experience

Preferred Qualifications

• IaC practical experience (Terraform / Pulumi, etc.)
• GPU cluster operations and optimization experience
• ML infrastructure / MLOps construction experience
• AI workload operations experience (inference servers, model serving)
• Event-driven architecture experience (Kafka / RabbitMQ, etc.)
• SRE / DevOps practices (SLI / SLO design, Chaos Engineering, etc.)
• Security engineering experience
• Technical communication ability in English