Lead Machine Learning Engineer

4 days ago

Singapore GRABTAXI HOLDINGS PTE. LTD. Full time
As a Lead MLOps Engineer, you will 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 rapidly train, validate, and deploy models. You will 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 will report to a Head of Engineering and contribute directly to both technical and strategic leadership.
The Day-to-Day Activities
  • 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 cross-functionally with engineers, 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.

What Essential Skills You Will Need
You have:
  • At least 5 years of extensive experience, in Robotics, Autonomous systems, Machine Learning, Statistics, Applied Mathematics, Computer Science, Economics, Operations Research, or a related fields.
  • Deep understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization.
  • Knowledge of model compression, quantization, and techniques for optimizing inference latency and cost. Familiarity with GPU/TPU acceleration and distributed inference architectures
  • Proficiency in deep learning frameworks (TensorFlow, PyTorch) and deployment tools (ONNX, tf-serving, TorchServe, Triton Inference Server)
  • Hands-on experience building and managing CI/CD pipelines for machine learning (e.g., GitLab CI, Kubeflow, MLflow)
  • Experience with model versioning, CI/CD for ML, containerization (e.g., Docker), and cloud-based deployment (AWS, GCP, Azure)
  • Proficient in one or more of the following programming languages: Python

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