Job Overview
Location
Seoul, Korea
Job Type
Full-time
Category
Software Engineering
Date Posted
June 4, 2026
Full Job Description
đź“‹ Description
- • Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment for a proactive AI smart assistant used by everyday users.
- • Fine-tune and adapt large language models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation to improve performance and reliability under real-world constraints.
- • Architect and operate scalable inference systems that balance latency, cost, and reliability while supporting multi-step reasoning and persistent context in production.
- • Design and maintain high-quality data systems for both synthetic and real-world training data to ensure model accuracy and robustness.
- • Implement comprehensive evaluation pipelines covering performance, robustness, safety, and bias in close partnership with research leadership.
- • Own production deployment responsibilities including GPU optimization, memory efficiency, latency reduction, and scaling policies for ML models.
- • Collaborate directly with application engineering teams to integrate ML systems cleanly into backend, mobile, and desktop products with clean APIs and minimal friction.
- • Make pragmatic trade-offs to ship improvements quickly while maintaining system correctness, reliability, and safety under production constraints.
- • Detect, debug, and resolve production issues rapidly to minimize user impact and ensure continuous system stability.
- • Ensure ML pipelines, training loops, and inference systems are stable, efficient, maintainable, and meet clear performance and quality targets.
- • Support and align team members to enable high-impact ML work with minimal friction and maximum autonomy.
- • Drive measurable iterations on models and systems that demonstrably improve user experience over time.
- • Work under real production constraints including strict latency, cost, reliability, and safety requirements for a product focused on long-running workflows and real-world task completion.
- • Translate research direction into production-grade ML systems that are trainable, deployable, observable, and performant in a live user environment.
- • Operate at the intersection of research, infrastructure, and product to bridge theoretical advancements with practical user outcomes.
- • Contribute to a high-talent-density, hands-on team that moves at rapid speed, values collective decision-making, and prioritizes shipping high-quality work while learning from real usage.
🎯 Requirements
- • Built or shipped real ML systems used by people, not just demos
- • Comfortable working with large models and understanding their failure modes
- • Write strong, production-grade code and care about system correctness
- • Self-directed, pragmatic, and take full ownership of outcomes
- • Communicate clearly and collaborate well in small, high-trust teams
- • Experience with PyTorch / JAX and GPU-based training and inference systems
🏖️ Benefits
- • Opportunity to build a truly magical AI product used by billions globally
- • Work in a high-talent-density, hands-on team with rapid decision-making and execution
- • Collaborate directly with research and engineering to bridge theory and real-world impact
- • Transparent and efficient interview process with prompt decisions
Skills & Technologies
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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