Machine Learning Engineer

Bjak Sdn. Bhd. (BJAK); United States; On-site; USD 130,000 - 180,000; Full time

Job summary

Bjak Sdn. Bhd. (BJAK) is seeking a full-time remote Machine Learning Platform Engineer to design systems for model training, deployment, and continuous improvement, collaborating with AI engineers and researchers to enhance production capabilities.

You will design and operate systems for training, evaluation, and deployment, build model serving infrastructure for high-throughput workloads, and develop data pipelines and monitoring for AI/ML workloads.

Qualifications

• Strong software engineering fundamentals applicable to production systems.
• Experience building ML infrastructure or production ML systems.
• Proficiency with model deployment, inference, and data pipelines.
• Understanding of distributed systems and system reliability.
• Ability to write clean, maintainable, production-quality code.

Responsibilities

• Design and operate systems for model training, evaluation, and deployment.
• Build and optimize model serving infrastructure for high-throughput and low-latency workloads.
• Develop reliable data pipelines and monitoring systems for AI/ML workloads.

Skills

Software engineering

ML infrastructure

Model deployment

Distributed systems

Clean code

Job description

Building and operating the ML infrastructure for AI products, the full-time remote Machine Learning Platform Engineer will design systems for model training, deployment, and continuous improvement while collaborating with AI engineers and researchers to enhance production capabilities.

Key responsibilities

• Design and operate systems for model training, evaluation, and deployment
• Build and optimize model serving infrastructure for high-throughput and low-latency workloads
• Develop reliable data pipelines and monitoring systems for AI/ML workloads

Required qualifications

• Strong software engineering fundamentals with experience in production systems
• Experience building ML infrastructure or production machine learning systems
• Proficiency in model deployment, inference, and data pipelines
• Understanding of distributed systems and system reliability
• Ability to write clean, maintainable, production-quality code