About SmartNews
SmartNews is a leading global information and news discovery company dedicated to delivering quality information to the people who need it. Thanks to our unique machine-learning technology and relationships with more than 3,000 global publisher partners, we provide news that matters to millions of users.
Founded in 2012 in Tokyo, SmartNews also has offices in Osaka (Kansai Office), Palo Alto, New York and Singapore.
If you share our vision and are passionate about our mission, we encourage you to apply!
Team Mission
The Delivery & ML Platform team builds and operates the delivery, ranking, experimentation, and machine learning platforms that enable SmartNews to deliver quality information reliably, efficiently, and at scale.
Our systems sit on the critical path between content, users, product experiences, ads, and machine learning. We own and evolve high-traffic backend services and platform capabilities such as delivery systems, ranking and mixing infrastructure, experimentation, ML feature and feedback pipelines, real-time training, ML serving integration, and AI-assisted operational tooling.
Our work directly affects how reliably users receive relevant content, how quickly product and ads teams can run experiments, and how effectively machine learning teams can improve ranking and personalization. We help product, ads, content, and machine learning teams move faster by providing platform capabilities that are scalable, observable, easy to operate, and safe to change.
We value end-to-end ownership. Our work is not only to build features, but also to make production systems more reliable, make changes easier to trace and roll back, reduce operational toil, and create self-service tools that help other teams solve problems without waiting on platform engineers.
About the Role
In this role, you will design, build, and operate platform systems that improve SmartNews' delivery, ranking, experimentation, and ML workflows.
You will turn ambiguous business and platform needs into practical engineering solutions: clarifying requirements, proposing designs, implementing backend services and internal tools, rolling them out safely, monitoring production impact, and iterating based on data and stakeholder feedback.
You will work closely with Product, Ads, Content, ML, and infrastructure teams to improve user-facing delivery reliability, accelerate ML and experimentation workflows, and strengthen the feedback loops between delivery systems and machine learning systems.
This is a hands-on engineering role for someone who enjoys high-scale backend systems, production ownership, cross-functional collaboration, and platform work that creates leverage for many other teams.
Responsibilities
- Design, implement, test, deploy, and operate backend/platform services for delivery, ranking, experimentation, ML feature and feedback pipelines, real-time training, and ML platform workflows.
- Improve reliability and operability of high-traffic production systems through observability, SLOs, alert quality, runbooks, performance tuning, capacity planning, and incident follow-up.
- Lead end-to-end engineering projects from problem definition and system design through implementation, rollout, monitoring, maintenance, and documentation.
- Partner with Product, Ads, Content, ML, and infrastructure stakeholders to translate business or ML needs into scalable platform capabilities.
- Build or improve internal tools and self-service workflows, including AI-assisted debugging, operations, and platform support where they create real leverage.
- Contribute to a healthy engineering culture by sharing context, reviewing designs and code, supporting on-call operations, and taking ownership beyond narrow component boundaries.
Requirements
Minimum requirements
- Strong backend engineering experience with large-scale, high-traffic, or low-latency production systems, using Java/Kotlin or comparable server-side languages.
- Solid system design skills for distributed services, APIs, data pipelines, and platform components, with clear reasoning about scalability, reliability, latency, cost, and maintainability trade-offs.
- Production ownership experience, including observability, monitoring, SLOs, incident response, performance tuning, deployment safety, and operating services after launch.
- Ability to navigate complex codebases and ambiguous requirements, break down problems, communicate design choices clearly, and deliver maintainable solutions end to end.
- Strong collaboration skills in English, with a team-oriented mindset, and openness to feedback.
Nice to have experiences/skills
- Experience with recommendation, ranking, feed delivery, ads delivery, personalization, or other user-facing decisioning systems.
- Experience with ML platform areas such as feature pipelines, real-time or batch training pipelines, ML serving, model monitoring, or online/offline experimentation.
- Experience with experimentation platforms, A/B testing infrastructure, metrics pipelines, or data-driven product iteration.
- Experience with cloud infrastructure, Kubernetes, Terraform, Datadog, SRE practices, or on-call operations for high-scale services.
- Japanese proficiency, frontend/internal-admin tool experience such as React, or open-source contributions.
Related Links
- "Build It Together" ― How Engineers, Designers, and PMs Shape Products at SmartNews.
- Why Shin Murakami Chose "SmartNews Now"
Working condition
- Office Location: Tokyo
- Click here or visit our careers site for more info.
Benefits
Benefits available at the SmartNews Tokyo Office
- All healthcare and social insurance required by the Japanese labor law, plus annual health check
- Visa sponsorship and overseas relocation support available for eligible candidates
- Click here or visit our careers site for more info about our benefits.
