Tokyo, Japan
Open 6 months, first seen by us 191 days agoArchitect and scale Spark batch and Flink streaming pipelines for ML training and evaluation
Design and operate ML job execution frameworks for training, inference, and post-processing
Build internal API servers and developer tools to orchestrate ML jobs on Kubernetes
Design and monitor ClickHouse and PostgreSQL data infrastructure
Mentor junior engineers and shape team engineering standards
Architect, implement, and scale batch (Spark) and streaming (Flink) pipelines that process billions of records daily for ML training and evaluation.
Design and operate robust ML job execution frameworks for training, inference, and post-processing.
Build and maintain internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform).
Design and monitor data infrastructure using ClickHouse and PostgreSQL.
Mentor junior engineers and help evolve team engineering culture and standards.
What We’re Looking For - Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s preferred. - 4+ years of hands-on experience in data systems, machine learning infrastructure, or platform engineering.
Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making.
In the employer’s words · Read in context
Open 6 monthsWe first saw this posting 191 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
Day 191
Up longer than most. Similar jobs usually come down between day 6 and day 70.
Well past Appier’s usual: its jobs come down by about day 77.
From 4,444 similar postings that came down, and 10 of Appier’s. Coming down is not always a hire.
15 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 4+ years of hands-on experience in data systems, machine learning infrastructure, or platform engineering. - Strong coding proficiency in Python and/or Java, with experience building large-scale production systems. - Practical experience with Spark, Flink, Kubernetes (GKE), and infrastructure-as-code tools such as Terraform and Helm.
- Build and maintain internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform). - Design and monitor data infrastructure using ClickHouse and PostgreSQL. - Ensure high availability and observability through monitoring tools like Prometheus and Grafana.
- Practical experience with Spark, Flink, Kubernetes (GKE), and infrastructure-as-code tools such as Terraform and Helm. - Experience managing high-throughput data infrastructure using ClickHouse, PostgreSQL, or similar systems. - Deep understanding of ML pipelines and distributed job execution in production environments.
- Design and monitor data infrastructure using ClickHouse and PostgreSQL. - Ensure high availability and observability through monitoring tools like Prometheus and Grafana. - Collaborate with data scientists, product managers, and engineers to deliver reliable and efficient ML platform capabilities.
The Impact You’ll Make at Appier We’re looking for a Staff/Senior Machine Learning Platform Engineer to join our Machine Learning Platform Team, which powers end-to-end infrastructure for model training, evaluation, deployment, and monitoring at scale. Our platform supports daily execution of hundreds of ML models and processes billions of data records across batch and streaming pipelines. In this role, you’ll shape the architecture and core components of our ML platform—covering batch (Spark), streaming (Flink), job orchestration (Argo on Kubernetes), and infrastructure tools—while ensuring the platform remains robust, scalable, and developer-friendly. You’ll also champion best practices and modern development tools including LLM-based programming assistants. What You’ll Work On
- Design and operate robust ML job execution frameworks for training, inference, and post-processing. - Build and maintain internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform). - Design and monitor data infrastructure using ClickHouse and PostgreSQL.
- Strong coding proficiency in Python and/or Java, with experience building large-scale production systems. - Practical experience with Spark, Flink, Kubernetes (GKE), and infrastructure-as-code tools such as Terraform and Helm. - Experience managing high-throughput data infrastructure using ClickHouse, PostgreSQL, or similar systems.
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Already applied? Track this application
Yes. We’ve followed one other Staff/Senior Software Engineer, Machine Learning Platform (Ad Cloud) job there since March 2026. It came down after 38 days.
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
About Appier
Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.
The Impact You’ll Make at Appier
We’re looking for a Staff/Senior Machine Learning Platform Engineer to join our Machine Learning Platform Team, which powers end-to-end infrastructure for model training, evaluation, deployment, and monitoring at scale. Our platform supports daily execution of hundreds of ML models and processes billions of data records across batch and streaming pipelines.
In this role, you’ll shape the architecture and core components of our ML platform—covering batch (Spark), streaming (Flink), job orchestration (Argo on Kubernetes), and infrastructure tools—while ensuring the platform remains robust, scalable, and developer-friendly. You’ll also champion best practices and modern development tools including LLM-based programming assistants.
What You’ll Work On
What We’re Looking For
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Tokyo, Japan
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