Site Reliability Engineer — ML Infrastructure

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 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.)