Machine Learning Engineer

Company 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.

Job Description

Get to know the Team

The mission of the ML Pipeline team at Grab is to empower machine learning engineers, data scientists, data analysts, and data engineers to test-and-learn their ideas and productionise them at scale. The team develops tools, systems and automation to increase productivity throughout the ML and AI development lifecycle.

Get to know the Role

As a Machine Learning Engineer in our ML Pipeline team, you will be responsible for contributing to the design, implementation, and rollout of cutting-edge ML&AI platforms for large-scale workloads at Grab.

The Critical Tasks You Will Perform

• Write production-grade code, perform code reviews and ensure exceptional code quality
• Build robust, lasting, and scalable products Iterate quickly without compromising quality
• Setup and define standards for complex pipelines including data engineering, feature engineering, model training, model quality verification, model deployment, etc.
• Automate cloud infrastructure provisioning and deployments of ML pipelines
  
  

Qualifications

What Essential Skills You Will Need

• A bachelors/Master degree in computer science, machine learning or related fields
• 3+ years of machine learning experience in industry
• Proficient in at least one programming language such as Golang, Python, Scala, or Java
• Strong understanding of machine learning approaches and algorithms
• Extensive knowledge of ML frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks
• Experiences of Docker, Kubernetes, Ray, NoSQL solutions, Memcache/Redis, cloud platforms (specifically, AWS)
• Familiarity with machine learning lifecycle management, including feature engineering, model training, validation, deployment, A/B testing, monitoring, and retraining
• Experienced in MLOps and managing production machine learning lifecycle is a plus
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning is a plus
• Prior working experience with building GenAI or llmops platforms is a plus
  
  

Additional Information

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.