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 execution of ML systems including data pipelines, training workflows, evaluation systems, inference architecture, and production deployment.
- • 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 efficiency.
- • Architect and operate scalable inference systems that balance latency, cost, and reliability under real-world production constraints.
- • Design and maintain data systems for high-quality synthetic and real-world training data to support model training and evaluation.
- • Implement evaluation pipelines that measure model performance, robustness, safety, and bias in partnership with research leadership.
- • Own production deployment responsibilities including GPU optimization, memory efficiency, latency reduction, and scaling policies for ML models.
- • Collaborate closely with application engineering teams to integrate ML systems cleanly into backend, mobile, and desktop products.
- • Make pragmatic trade-offs to ship improvements quickly while maintaining system correctness and user safety.
- • Debug and resolve production issues rapidly to minimize user impact and ensure system reliability.
- • 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 consistently improve user experience over time.
- • Work under real production constraints including strict latency, cost, reliability, and safety requirements.
- • Translate research direction into production-grade ML systems that are trainable, deployable, observable, and performant.
- • Operate in a high-talent-density, hands-on team environment that values collective decision-making, rapid iteration, and independent execution.
- • Contribute to building a proactive AI smart assistant focused on long-running workflows, persistent context, and real-world task completion with high reliability.
- • Ensure models handle multi-step reasoning and interact reliably with external tools despite non-deterministic behavior.
🎯 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 Python, PyTorch/JAX, and GPU-based training and inference systems
🏖️ Benefits
- • Work on a high-impact product bringing AI practical benefits to billions globally
- • Join a small, world-class team with high talent density and hands-on culture
- • Collaborate in a fast-moving environment that balances shipping quality work with learning
- • Transparent and efficient interview process with prompt decision-making
- • Opportunity to shape the execution layer of a proactive AI smart assistant
- • Direct impact on product reliability, safety, and user experience
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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