Get to Know the Team
You'll join a team building production-grade robotics and autonomy systems for urban environments across Southeast Asia. We advance perception, planning, and control capabilities step by step, using safety evidence as the gate for every milestone. We focus on building reliable systems, scaling without compromising quality, and collaborating with industry partners while developing in-house expertise where it sets us apart.
We are a senior, hands-on engineering group that values clear interfaces and reproducible pipelines.
Get to Know the Role
As a Lead MLOps Engineer, you'll report to the Head of Engineering and work from our One North Singapore office. You'll contribute to both technical leadership and strategic decisions for our foundational infrastructure team. Your mission is to develop the pipelines and workflows that help the team to train, validate, and deploy models. You'll have hands-on ownership of key components of our MLOps and simulation platform.
The Critical Tasks You Will Perform
- You'll design and implement data pipelines that ingest, transform, and curate robotics datasets (sensor data, logs, annotations) for model training and validation workflows.
- You'll build MLOps workflows that automate model training, track experiments, version datasets, and ensure reproducible training runs across the team.
- You'll develop deployment infrastructure that packages models for production, manages model artifacts, and orchestrates inference serving for real-time robotics applications.
- You'll create internal tools and APIs that modelling engineers use to submit training jobs, monitor progress, and access trained models.
- You'll improve inference pipelines for latency and throughput, implementing model compression and hardware acceleration (GPU/TPU) techniques for production robotics systems.
- You'll design monitoring and alerting systems that track model performance, data drift, and system health in production environments.
- You'll guide team members on MLOps architecture decisions, review technical designs, and establish standards for machine learning infrastructure.