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Sovereign’s Capital
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Jobs

Senior Machine Learning Engineer

Grab

Grab

Software Engineering
Petaling Jaya, Selangor, Malaysia
Posted on Friday, May 31, 2024

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 Senior Machine Learning Engineer in our ML Pipeline team, you will be responsible for designing, implementing, rolling out, and evangelizing cutting-edge ML&AI platforms for large scale workloads at Grab.

The Day-to-Day Activities

  • Write production-grade code, at scale
  • Develop platform applications from infrastructure to frontend in full stack
  • 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
  • Reason about how to use appropriate frameworks, algorithms, and data structures

Qualifications

The Must-Haves

  • 4+ years of relevant experience in machine learning engineering, mlops, llmops or similar roles
  • Proficient in at least one programming language such as Golang, Python, Scala, or Java
  • Prior experience designing, implementing, and deploying large-scale machine learning systems
  • Extensive knowledge of ML frameworks such as TensorFlow, PyTorch, Spark, etc
  • Understanding of distributed systems, cloud platforms (specifically, AWS), and containerization technologies like Kubernetes
  • Familiarity with machine learning lifecycle management, including feature engineering, model training, validation, deployment, A/B testing, monitoring, and retraining
  • Prior working experience with building GenAI or llmops platforms is a big plus
  • Strong analytical, critical thinking, and communication skills

Additional Information

Our Commitment

We recognize that with these individual attributes come different workplace challenges, and we will work with Grabbers to address them in our journey towards creating inclusion at Grab for all Grabbers.