Senior Machine Learning Engineer

insignia.co.id; Indonesia; Hybrid; IDR 223,200,000 - 390,600,000; Full time

Job summary

Insignia is seeking a Machine Learning Engineer to bridge data science and production engineering. You will design, train, and deploy production-grade ML systems powering real-world intelligence from data ingestion to API serving.

The role is hybrid in West Jakarta, with a focus on scalable pipelines and efficient inference. You will collaborate with data engineers and backend teams, optimize models for latency and cost, and implement robust MLOps practices to ensure reliability and

Qualifications

• 2–5 years of experience in machine learning engineering or applied AI.
• Proficient in Python, PyTorch/TensorFlow, and modern ML frameworks.
• Experience deploying models with FastAPI, Flask, Docker, or cloud endpoints.
• Familiar with MLOps tools (MLflow, Kubeflow, SageMaker, or similar).
• Solid software engineering practices (testing, CI/CD, system design).
• Fluent in English — written and spoken.
• Pragmatic, systems-oriented, and value-driven about real-world AI.

Responsibilities

• Design, train, and deploy production-grade ML systems from data ingestion to API serving.
• Build and maintain ML pipelines for training, validation, and continuous serving.
• Optimize models for performance, cost, and scalability (quantization, batching, caching).
• Implement monitoring for data drift, model decay, and system health.
• Collaborate with data engineers and backend teams to integrate ML into products.
• Establish best practices for MLOps, versioning, and reproducibility.

Skills

Python

PyTorch

TensorFlow

FastAPI

MLflow

Kubeflow

CI/CD

Cloud platforms

Tools

Docker

Kubernetes

Job description

At Insignia, we’re looking for a Machine Learning Engineer to bridge the gap between data science and production engineering.

You won't just be training models in a notebook. You’ll be building scalable pipelines, optimizing inference, and ensuring reliability at scale. If you care about latency, cost, uptime, and making sure your models actually deliver value to the end-user, this is your kind of challenge.

This is a hybrid role based in West Jakarta, where your code powers real-world intelligence.

What You’ll Do:

• Design, train, and deploy production-grade ML systems from data ingestion to API serving
• Build and maintain ML pipelines for training, validation, and continuous serving
• Optimize models for performance, cost, and scalability (quantization, batching, caching)
• Implement monitoring for data drift, model decay, and system health
• Collaborate with data engineers and backend teams to integrate ML seamlessly into products
• Establish best practices for MLOps, versioning, and reproducibility

Who You Are:

• 2–5 years in machine learning engineering or applied AI
• Strong in Python, PyTorch/TensorFlow, and modern ML frameworks
• Experience with model deployment (FastAPI, Flask, Docker, cloud endpoints)
• Familiar with MLOps tools (MLflow, Kubeflow, SageMaker, or similar)
• Deep understanding of software engineering principles (testing, CI/CD, system design)
• Fluent in English — written and spoken
• Pragmatic, systems-oriented, and passionate about building AI that actually works in the wild

Why Join Us?

Because great AI isn’t measured in papers — it’s measured in uptime, user trust, and real-world utility. If you're ready to ship intelligence at scale, let’s talk.