About TableCheck
TableCheck, Japan's leading restaurant reservation management platform, is seeking a Site Reliability Engineer with machine learning expertise. As a member of our team, you will own the technology stack and help support our demanding business and developer needs. We run a robust and fault-tolerant infrastructure built on Amazon Web Services (AWS) with Terraform, Kubernetes, Helm, and an array of tools for CI/CD, logging, monitoring, and more. We emphasize DevOps best practices such as agile, scrum, automation, and customer-centric improvements.
TableCheck has embraced remote work. As such, communication and documentation are in our blood. We look for and write about signals in the noise which enables us to constantly learn from mistakes and adapt, and we expect members of our teams to constantly follow up with questions and updates to keep everyone in the loop. You can read more about Working at TableCheck as an SRE
Role Overview
This role is primarily focused on SRE responsibilities — maintaining and evolving our production infrastructure on AWS and Kubernetes. You will also contribute to machine learning initiatives, bringing reliability engineering discipline to ML pipelines, model deployment, and the infrastructure that powers intelligent features on our platform.
Responsibilities
SRE (Primary Focus)
Following SRE principles to maintain a 24/7 production environment running on Kubernetes
Implementation of DevOps methodologies to improve IT team quality of life
Proactive system monitoring and configuration
Incident response and postmortem processes
Managing and evolving AWS infrastructure (EKS, EC2, RDS, Fargate, CloudFront, Lambda, S3)
Building and maintaining CI/CD pipelines, infrastructure as code (Terraform, Helm, ArgoCD)
Ensuring system reliability, performance, and scalability across our production stack
Machine Learning
Applying SRE discipline to ML infrastructure — ensuring model serving, training pipelines, and data systems are reliable, observable, and well-operated
Supporting and improving ML model deployment pipelines and MLOps practices
Monitoring ML model performance in production and building alerting and observability for ML systems
Collaborating with data scientists and product teams to operationalize ML models at scale
Contributing to infrastructure for ML workloads on Kubernetes and AWS
Mandatory Skills
At least 2 years of experience with Amazon Web Services (AWS), with particular focus on EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3
Extensive hands-on experience using AWS EKS
Experience in direct software engineering following DevOps / SRE practices with at least 1 year as a technical lead
Current ability in at least one of the following languages: Python, Ruby, Elixir, Go, Javascript, Rust
Understanding of container and hypervisor fundamentals
Configuration management (YAML / Bash); experience with Helm and Terraform preferred
Experience running production systems at large scale, and an understanding of the kinds of problems that can occur along with likely solutions
Familiarity with machine learning workflows and MLOps practices
Python experience with ML-adjacent tooling (model deployment, inference serving, or ML pipeline tooling)
Recommended Skills
Previous startup experience is highly desired
Terraform, Pulumi
ArgoCD
Prometheus and Grafana for monitoring and alerting
PostgreSQL and MongoDB
Kafka for event-driven architecture
Security, PCI-DSS, GDPR, forensics
Experience with ML model serving frameworks (e.g., TensorFlow Serving, TorchServe, Triton)
Familiarity with feature stores, experiment tracking, or model registry tools
Experience deploying and managing ML workloads on Kubernetes
Language Skills
A native level of English is required. (No Japanese skill is required for this role.)
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