Staff Engineer, Production ML Systems

ActAI; Indonesia; On-site; IDR 480,000,000 - 800,000,000; Full time

Job summary

ActAI is seeking a Staff Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will lead across the model lifecycle—data, training, evaluation, inference, and deployment—and collaborate with research and application teams to deliver production-ready ML features.

You will architect high-performance inference systems, optimize GPU utilization, and build robust data pipelines for real-world

Qualifications

• Experience building and shipping ML systems in production.
• Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.
• Strong software engineering and systems fundamentals.

Responsibilities

• Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.
• Build and evolve training and fine-tuning pipelines for large models.
• Design evaluation systems that measure capability, robustness, safety, and real-world product performance.
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.
• Build data pipelines and systems for high-quality real-world and synthetic training data.
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.
• Partner closely with research and application engineering to turn model capabilities into product improvements.
• Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.

Skills

Python

GPU-based training

Large-model training

Tools

PyTorch

JAX

Job description

ActAI is seeking a Staff Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will lead across the model lifecycle—data, training, evaluation, inference, and deployment—and collaborate with research and application teams to deliver production-ready ML features.

You will architect high-performance inference systems, optimize GPU utilization, and build robust data pipelines for real-world