Production ML Engineer - Real-World Systems

Businesslist; Johor; On-site; MYR 90,000 - 140,000; Full time

Job summary

Businesslist is seeking a Machine Learning Engineer in Johor to build and improve ML components across data, training, evaluation, and inference within our production systems.

You will collaborate with senior ML engineers, product teams, and research to deliver reliable ML-powered features under real production constraints. A strong foundation in ML and hands-on experience with training, fine-tuning, or deploying models are essential.

Qualifications

• Proficient in Python with experience in PyTorch or JAX for ML models.
• Experience deploying ML models in production with GPUs.
• Strong grounding in machine learning fundamentals and neural architectures.
• Ability to write production-quality code and quickly pick up new tools.
• Curious, coachable, and willing to own issues in real systems.
• Bias toward shipping, iteration, and continuous improvement.

Responsibilities

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models within production systems.
• Implement evaluation and testing to understand model behavior.
• Help build and maintain data pipelines for real-world and synthetic data.
• Debug model issues, performance problems, and production incidents.
• Ship improvements iteratively and learn from real user feedback.
• Collaborate with senior ML engineers and product teams.
• Ensure latency, cost, reliability, and safety constraints are met.
• Produce robust, reproducible ML pipelines and systems.

Skills

Python

PyTorch

JAX

Production ML systems

Job description

Businesslist is seeking a Machine Learning Engineer in Johor to build and improve ML components across data, training, evaluation, and inference within our production systems.

You will collaborate with senior ML engineers, product teams, and research to deliver reliable ML-powered features under real production constraints. A strong foundation in ML and hands-on experience with training, fine-tuning, or deploying models are essential.