
51 - 200 employees
🛍️ eCommerce
🏪 Marketplace
eCommerce • Insurance • Marketplace
BJAK is a leading online platform in Southeast Asia that offers comprehensive automobile insurance comparison services. The company enables Malaysian users to compare and purchase auto insurance from multiple insurers efficiently, providing considerable savings and convenience. BJAK is renowned for its user-friendly digital platform that allows quick insurance and road tax renewals, offering discounts up to 11%. With a strong emphasis on customer service, BJAK also provides 24/7 roadside assistance, accident support, and replacement vehicles. It is a pioneer in the insurance comparison sector in the region and has facilitated significant savings for millions of car owners.
🕒 February 11
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51 - 200 employees
🛍️ eCommerce
🏪 Marketplace
eCommerce • Insurance • Marketplace
BJAK is a leading online platform in Southeast Asia that offers comprehensive automobile insurance comparison services. The company enables Malaysian users to compare and purchase auto insurance from multiple insurers efficiently, providing considerable savings and convenience. BJAK is renowned for its user-friendly digital platform that allows quick insurance and road tax renewals, offering discounts up to 11%. With a strong emphasis on customer service, BJAK also provides 24/7 roadside assistance, accident support, and replacement vehicles. It is a pioneer in the insurance comparison sector in the region and has facilitated significant savings for millions of car owners.
• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety
• 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. • Comfort owning ambiguous, zero-to-one ML systems end-to-end. • A bias toward shipping, learning fast, and improving systems through iteration.
• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development
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