Technical Lead, Machine Learning

🕒 February 11

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BJAK

WebsiteLinkedInAll Job Openings

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.

📋 Description

• Own the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment • Architect and operate scalable inference systems, balancing latency, cost, and reliability • Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment

🎯 Requirements

• You have built or shipped real ML systems used by people, not just demos • You are comfortable working with large models and understanding their failure modes • You write strong, production-grade code and care about system correctness • You are self-directed, pragmatic, and take full ownership of outcomes • You communicate clearly and collaborate well in small, high-trust teams

🏖️ Benefits

• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership • Design and maintain data systems for high-quality synthetic and real-world training data • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation • Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment • Work under real production constraints: latency, cost, reliability, and safety • Make pragmatic trade-offs and ship improvements quickly, learning from real usage