Senior ML Infrastructure Engineer - Kubernetes & Scalable AI

MOTIONAL SINGAPORE PTE. LIMITED; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

MOTIONAL SINGAPORE PTE. LIMITED is seeking a Senior Software Engineer to design, build, and deploy core components of our ML infrastructure platform on Kubernetes.

You will develop robust services covering the ML lifecycle, from data ingestion to model evaluation, and own features end-to-end from design to production. You should have 4+ years in software engineering, strong Python/Go skills, and hands-on Kubernetes deployment experience, with cloud exposure (AWS, GCP, or Azure).

Qualifications

• 4+ years of professional software engineering experience.
• BS or MS in Computer Science or related field.
• Hands-on experience deploying applications on Kubernetes.
• Experience with high-scale infrastructure or distributed backend systems.
• Strong proficiency in Python, Go, or a similar language.
• Experience with a major cloud provider (AWS, GCP, Azure).

Responsibilities

• Design, build, and deploy core components of our ML infrastructure platform on Kubernetes.
• Develop robust and scalable services that support the entire ML lifecycle, from data ingestion and processing to model training and evaluation.
• Write high-quality, maintainable code for high-throughput systems that handle petabytes of data.
• Own features and systems end-to-end, driving them from initial design through to production deployment and operation.

Skills

Kubernetes

Python

Go

Distributed systems

Cloud (AWS, GCP, Azure)

Software design

Education

BS/MS in Computer Science or related field

Tools

Ray

Job description

MOTIONAL SINGAPORE PTE. LIMITED is seeking a Senior Software Engineer to design, build, and deploy core components of our ML infrastructure platform on Kubernetes.

You will develop robust services covering the ML lifecycle, from data ingestion to model evaluation, and own features end-to-end from design to production. You should have 4+ years in software engineering, strong Python/Go skills, and hands-on Kubernetes deployment experience, with cloud exposure (AWS, GCP, or Azure).