Company Description AIRR Labs is a leading Southeast Asia e-commerce enabler headquartered in Singapore, helping FMCG, Beauty, and Lifestyle brands grow across platforms such as TikTok Shop, Shopee, and Lazada. Founded in 2019, the company has a team of 350+ specialists across Thailand, the Philippines, Vietnam, Malaysia, and Singapore, supported by a technology hub in Shanghai. AIRR Labs provides integrated end-to-end digital commerce solutions, including content strategy, performance marketing, marketplace store management, short-form video production, livestreaming, affiliate management, and multi-channel fulfilment. The company is a certified TikTok Strategic Agency, Shopee Premium Enabler, and Lazada Star Enabler, and is trusted by global brands such as Unilever, Colgate-Palmolive, Estée Lauder, and Adidas. AIRR Labs focuses on simplifying and elevating 360° digital commerce for its partners in the region.
Role Description The Machine Learning Engineering Manager is a full-time, hybrid role based in Bangkok, with flexibility for partial work from home. This role oversees the design, development, and deployment of machine learning models and data-driven systems that power AIRR Labs digital commerce solutions, including recommendation engines, predictive analytics, and performance optimization tools. The manager leads and mentors a team of ML engineers and data scientists, sets technical direction, and ensures high-quality, scalable implementations in collaboration with product, engineering, and business stakeholders. Day-to-day responsibilities include reviewing and improving model architectures, managing the ML lifecycle from experimentation to production, establishing best practices for data pipelines and MLOps, and monitoring performance to drive continuous improvement. The role also involves aligning ML initiatives with business goals, communicating insights to non-technical partners, and contributing to the long-term technology roadmap.
Qualifications
- Strong foundation in machine learning and data science, including experience with supervised and unsupervised learning, recommendation systems, and predictive modeling.
- Proficiency with ML frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and experience building production-grade models and pipelines.
- Solid software engineering skills, including Python or similar languages, version control (Git), testing, and familiarity with microservices or distributed systems.
- Experience with MLOps practices and tools, such as model deployment, monitoring, CI/CD, and working with cloud platforms (e.g., AWS, GCP, or Azure).
- Background in data engineering concepts, including data warehousing, ETL/ELT pipelines, and working with large-scale datasets.
- Proven leadership experience managing or mentoring ML or engineering teams, with the ability to set priorities and drive projects to completion.
- Strong communication
