MLOps Engineer

MISSION

As the founding MLOps engineer, design and build Shizuku’s ML infrastructure from the ground up. Establish the complete pipeline — from data ingestion through training environments to model serving — creating an internal platform that empowers ML engineers to iterate on models at maximum velocity.

Replace individual, siloed development environments with a unified team-scale ML development platform, maximizing the speed of Shizuku’s evolution.

ABOUT SHIZUKU

Shizuku is a Japan-born AI companion actively engaging audiences on YouTube and X (formerly Twitter). Already running live streams and cultivating a growing community, Shizuku is now entering its next phase of rapid scale.

As the first Japanese startup to receive investment from a16z, we closed our seed round and are on a mission to bring Japanese entertainment × AI to the global stage.

TEAM STRUCTURE

You will work closely with founder Aki (ML engineer and researcher, ex-Meta, ex-Luma AI) and Engineering Director Ohno to drive the design and construction of our ML infrastructure. As the first MLOps engineer, you’ll have significant autonomy — from technology selection to operational design.

Post-foundation, career paths include both a management track leading a growing team and an IC track deepening technical expertise, tailored to your aspirations.

CURRENT STATE & WHAT YOU’LL BUILD

• Infrastructure Status: Modern application infrastructure is in place, but ML training and MLOps tooling are not yet established. AWS adoption is planned
• What You’ll Build: An internal platform for ML engineers developing Shizuku’s AI models. The goal: eliminate siloed, ad-hoc local workflows and code ownership by individuals, replacing them with a team-oriented ML development foundation

KEY RESPONSIBILITIES

• Design, build, and operate the end-to-end ML training pipeline: data collection/preprocessing → training → evaluation → deployment
• Design and build GPU training infrastructure on AWS (A100, L4, etc.) with cost optimization
• Build an internal ML platform for engineers: experiment tracking, model versioning, and reproducibility guarantees
• Design and build model serving infrastructure: inference APIs, auto-scaling, and latency management
• Establish training data management and quality assurance pipelines
• Design and implement CI/CD for ML: automated training, model testing/evaluation, and staged rollouts
• Drive production integration of models in collaboration with ML Engineer and SWE teams
• Build monitoring and visibility infrastructure for long-term compute cost and GPU utilization tracking

REQUIREMENTS

• 3+ years of experience designing, building, and operating cloud infrastructure on AWS, GCP, or equivalent platforms
• Experience building ML/DL pipelines and infrastructure
• Hands-on experience designing and operating production environments using container technologies (Docker/Kubernetes)
• Experience managing infrastructure as code (Terraform, Pulumi, etc.)
• Strong Python skills for building tools and pipelines
• Ability to work on-site at our Tokyo office (primarily in-office with flexible remote arrangements)

NICE TO HAVE

• Experience building, operating, and cost-optimizing GPU clusters (A100, H100, L4, etc.)
• Experience with ML platforms: SageMaker, Vertex AI, Ray, Kubeflow, etc.
• Experience deploying and operating experiment tracking infrastructure: MLflow, Weights & Biases, DVC, etc.
• Experience building model serving infrastructure: Triton Inference Server, TorchServe, vLLM, SGLANG, etc.
• Experience designing and building internal ML development platforms
• Domain-specific knowledge of ML workloads in speech, NLP, or vision
• Experience as a founding infrastructure/MLOps engineer at a startup
• Technical communication skills in English (currently Japanese-first internally; transitioning to a global environment in the mid-term)

WHO YOU ARE

• Founding Engineer Mentality — You don’t wait for established systems to improve — you define the design philosophy and build the foundation from zero. You’re energized by creating the system itself, not just refining one
• ML-Literate Infrastructure Engineer — You understand the unique characteristics of ML training and inference workloads, and you translate that understanding into optimally designed infrastructure
• Purpose-Driven Ownership — You reverse-engineer from “maximizing ML team velocity,” set your own priorities, and drive execution autonomously
• Comfort with Ambiguity — You design for a world where model count, training frequency, and data volume are still being defined — starting small and scaling architecturally as the picture clarifies
• Resilience & Respect — You engage as an equal partner with ML Engineers and SWEs, elevating the entire team’s productivity through collaboration