2 months ago
Seoul, Korea, SouthStaff+
Responsibilities
- Own end-to-end ML system execution across data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
- Fine-tune and adapt models using LoRA, QLoRA, SFT, DPO, and distillation.
- Architect and operate scalable inference systems while balancing latency, cost, and reliability.
- Design and maintain synthetic and real-world training data systems.
- Implement evaluation pipelines for performance, robustness, safety, and bias in partnership with research leadership.
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
- Collaborate with application engineering to integrate ML systems into backend, mobile, and desktop products.
- Detect, debug, and resolve production issues quickly while shipping measurable and safe improvements.
Requirements
- Built or shipped real machine learning systems used by people rather than only demos.
- Comfortable working with large models and understanding their failure modes.
- Writes strong production-grade code and cares about system correctness.
- Self-directed, pragmatic, and willing to take full ownership of outcomes.
- Communicates clearly and collaborates effectively in small, high-trust teams.
Categories
About Bjak
Bjak builds an online insurance marketplace for consumers in Malaysia and Southeast Asia, enabling comparison and purchase of car, health, life, and travel policies. Its business model is aggregator-based, earning commissions and fees from insurers and partners while streamlining digital applications and renewals. Founded in 2019 and headquartered in Selangor, it is privately held and is expanding from insurance comparison into broader fintech services via a planned finance super app.