Lead Machine Learning Engineer

4 days ago

Singapore Grab Full time

**Job Description**:
**Life at Grab**

At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.

**Get to know the Team**

The AI Automation team at Grab focuses on enabling machine learning and AI within Grab. This team is critical in making AI accessible to different departments, enhancing automated decision-making and personalisation capabilities.

**Get to know the Role**

As a Lead Machine Learning Engineer in our AI Automation team, you will be responsible for designing, implementing, rolling out, and evangelising cutting-edge machine learning platforms at Grab.

**The Day-to-Day Activities**
- Design and implement machine learning pipelines that cater to both functional and non-functional requirements
- Work closely with data scientists and engineers to deploy machine learning models efficiently
- Collaborate on the development of tools that automate and scale machine learning workflows
- Ensure that the ML automation platform meets high standards of quality, reliability, and performance
- Lead and conduct code reviews, design reviews, and architectural planning
- Mentor junior engineers and actively participate in growing the talent pool at Grab
- Contribute to the continuous improvement of machine learning practices across the organisation

**The Must-Haves**
- 4+ years of relevant experience in machine learning engineering or similar roles
- Prior experience designing, implementing, and deploying large-scale machine learning systems
- Proficient in at least one programming language used in machine learning such as Python, R, or Java
- Extensive knowledge of ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Strong understanding of distributed systems, cloud platforms (specifically, AWS and Azure), and containerization technologies like Kubernetes
- Familiarity with machine learning lifecycle management, including model training, validation, deployment, monitoring, and retraining
- Strong analytical, critical thinking, and communication skills

**Our Commitment**

We are committed to building diverse teams and creating an inclusive workplace that enables all Grabbers to perform at their best, regardless of nationality, ethnicity, religion, age, gender identity or sexual orientation and other attributes that make each Grabber unique.

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