Lead Machine Learning Platform Engineer

7 hours ago


Singapore PLT Engineering Full time

Get to know the Team Our team develops production-grade robotics and autonomy capabilities for the urban environments of Southeast Asia. We are advancing perception, planning, and control step by step, with safety evidence as the gate for every milestone. We focus on building robust system capabilities, scaling with uncompromised quality, and collaborating with industry leaders while investing in in-house expertise where it differentiates us. We are a senior, hands-on engineering group that prizes operational excellence, clear interfaces, and reproducible pipelines. Get to know the Role As a Lead MLOps Engineer, you\'ll be a core member of our foundational infrastructure team. Your mission is to lead the development of critical pipelines and workflows that enable the entire team to train, validate, and deploy models. You\'ll have hands-on ownership of essential components of our MLOps and simulation platform, directly impacting the team\'s development velocity and the reliability of our systems. You\'ll be based at Grab One North Singapore office and report to a Head of Engineering, contributing directly to both technical and strategic leadership. The Critical Tasks You Will Perform Own and operate the data pipelines responsible for ingesting, processing, and curating robotics datasets. Lead the design and implementation of our core MLOps workflows, including CI/CD for model training, data versioning, and large-scale validation. Partner with modeling engineers to understand their requirements and build the tools and services that accelerate their research and development. You participate in code and design reviews to maintain our high development standards. You engage in service capacity and demand planning, performance analysis, tuning, and optimization. Collaborate with engineers and data scientist teams to translate business needs into data solutions, implementing and deploying these solutions at scale. Mentor other engineers through code reviews, design discussions, and knowledge sharing. #J-18808-Ljbffr



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