Senior Machine Learning Engineer

Bjak Sdn. Bhd.

Job Overview

Location

Seoul, Korea

Job Type

Full-time

Full Job Description

📋 Description

• Build and own core machine learning systems that power a proactive, long-horizon AI assistant used by everyday users for conversations, errands, organization, and workflows.
• Take end-to-end ownership of ML subsystems in production, including data preparation, model training, evaluation, inference, and iterative refinement.
• Turn research ideas into reliable, production-grade systems that operate under real-world constraints such as latency, cost, reliability, and safety.
• Debug model failures and system issues using real production signals to identify root causes and implement fixes with minimal disruption.
• Ship, measure outcomes, refine, and repeat in rapid cycles to drive continuous improvement in ML system performance and user impact.
• Collaborate closely with research, product, and engineering teams to ensure seamless integration of ML features into the product and alignment with business goals.
• Mentor and review work of other ML engineers through technical judgment, code examples, and architectural guidance to raise the team’s overall engineering standard.
• Design and maintain robust, scalable, and maintainable training, inference, and data pipelines that support high reliability for long-running workflows and persistent context.
• Ensure ML models consistently meet targets for accuracy, latency, efficiency, and reliability under non-deterministic model behavior and complex multi-step reasoning scenarios.
• Interact with external tools and systems to enable the AI assistant to complete real-world tasks with high fidelity and consistency.
• Operate independently with high ownership, bringing structure and judgment to ambiguous problems and driving work to completion without constant oversight.
• Apply deep understanding of how modern ML models behave — and misbehave — in production environments to anticipate failure modes and design resilient systems.
• Write production-quality code in Python, prioritizing system thinking over scripting, and ensure all code is maintainable, testable, and scalable.
• Use GPU-based training and inference systems to optimize performance and resource utilization across the ML stack.
• Leverage real-world user feedback and production metrics to drive measurable improvements in ML system outcomes and product value.

🎯 Requirements

• Built and shipped ML systems used by real users in production environments
• Deep understanding of how modern ML models behave and misbehave in production
• Strong production-quality coding skills in Python with systems-level thinking
• Experience owning ML workflows end-to-end: data, training, evaluation, inference, iteration
• Ability to work independently, drive projects to completion, and mentor peers
• Experience with PyTorch or JAX and GPU-based ML infrastructure

🏖️ Benefits

• Opportunity to work on a high-impact AI product with global user reach
• Collaborative, high-talent-density team environment with rapid decision-making
• Hands-on role with direct ownership of critical production systems
• Transparent and efficient interview process with prompt feedback
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• "reasoning": "The job title is explicitly 'Senior Machine Learning Engineer' and the description focuses exclusively on building, deploying, and maintaining production ML systems using PyTorch/JAX, GPU infrastructure, and end-to-end ownership of ML workflows — all core to the Machine Learning Engineer role. No ambiguity exists in categorization.",
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Bjak Sdn. Bhd.

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About Bjak Sdn. Bhd.

Bjak operates Malaysia’s largest digital auto-insurance marketplace, enabling instant price comparison and online purchase of motor coverage from leading insurers. The platform uses proprietary technology to simplify complex tariffs, deliver personalised quotes and e-policy issuance within minutes, eliminating paperwork and agent visits. Licensed by Bank Negara Malaysia, Bjak also offers road-tax renewal, accident assistance and claims support, serving millions of drivers nationwide while partnering with insurers to increase digital distribution efficiency and customer reach.

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