Lead ML Engineer: Production-Scale AI Systems

Bjak; Indonesia; On-site; IDR 450,000,000 - 750,000,000; Full time

Job summary

Bjak is seeking a Lead Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.

Qualifications

• Experience building and shipping ML systems in production.
• Strong software engineering and systems fundamentals.
• Experience with large-model training, fine-tuning, evaluation, and inference.
• Ability to operate across data, training, inference, and deployment.

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

ML systems

Production code

GPU workloads

Model evaluation

Research to prod

Tools

Python

PyTorch

JAX

GPU-based systems

Job description

Bjak is seeking a Lead Engineer, Machine Learning to own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.