Job Overview
Location
Seoul, Korea
Job Type
Full-time
Category
Software Engineering
Date Posted
June 4, 2026
Full Job Description
đź“‹ Description
- • Build and own end-to-end machine learning pipelines spanning data collection, training, evaluation, inference, and production deployment for a proactive AI smart assistant.
- • Fine-tune and adapt large language models using state-of-the-art methods including LoRA, QLoRA, SFT, DPO, and model distillation to improve task performance and reliability.
- • Architect and operate scalable inference systems that balance latency, cost, and reliability under real-world production constraints.
- • Design and maintain high-quality data systems for both synthetic and real-world training data to support model training and evaluation.
- • Implement comprehensive evaluation pipelines that measure model performance, robustness, safety, and bias in partnership with research leadership.
- • Own production deployment of ML systems, including GPU optimization, memory efficiency improvements, quantization, mixed precision, and scaling policies.
- • Collaborate closely with application engineering teams to integrate ML systems cleanly into backend, mobile, and desktop product environments.
- • Make pragmatic trade-offs to ship iterative improvements quickly, learning from real user interactions and production feedback.
- • Operate under strict production constraints including low-latency requirements, cost efficiency, system reliability, and safety compliance.
- • Translate research direction into production-grade ML systems with a focus on long-running workflows, persistent context, and real-world task completion.
- • Ensure model behavior remains reliable despite non-deterministic outputs by implementing robust error handling, fallback mechanisms, and monitoring.
- • Develop and maintain evaluation tooling to continuously assess model behavior across diverse use cases and edge scenarios.
- • Optimize inference systems for multi-step reasoning and external tool interactions critical to the assistant’s ability to complete complex workflows.
- • Work independently in an ambiguous, zero-to-one environment to define and execute technical roadmaps for ML infrastructure.
- • Contribute to a high-talent-density team that values rapid iteration, collective decision-making, and shipping high-quality, magical user experiences.
🎯 Requirements
- • Strong background in deep learning and transformer-based architectures.
- • Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
- • Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.
- • Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).
- • Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.
- • Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
🏖️ Benefits
- • Opportunity to work on a high-impact AI product bringing practical intelligence to billions of users.
- • Collaborate with a high-talent-density, hands-on team that values rapid iteration and collective decision-making.
- • Work in an environment that prioritizes shipping high-quality work while learning from real user feedback.
- • Transparent and efficient interview process with prompt decision-making.
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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