Lead Machine Learning Engineer

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

Join our dynamic Ads Serving Team, where we are dedicated to ensuring that the right ads reach the right audience at the right moment. We lead algorithm improvements, focusing on enhancing ads inventory efficiency to maximise value for both advertisers and users. We take pride in managing the entire ads backend tech stack, which is designed to handle high query per second (QPS) loads, ensuring seamless performance.

Our work is not just about maintaining systems; we excel at solving complex challenges that arise in the world of digital advertising. If you're passionate about technology in the advertising landscape, you will find a home with us

Get to Know the Role

As a Lead Machine Learning Engineer, you will report into the Head of Engineering and work onsite in Grab One North Singapore Office. This is a hands-on role involving building large-scale machine learning, control theory and optimisation solutions. You will build data-driven technologies to drive real-world impact in Grab's ads business that consists of hundreds of thousands of consumers and merchants.

The Critical Tasks You Will Perform

• You will partner with product teams and other engineers to solve Ads business problems through Machine Learning.
• You will drive the set direction and goals, concerning project impact, Machine Learning system design, and Machine Learning excellence.
• You will re-evaluate the tradeoffs of already shipped features/ Machine Learning systems, and you can drive efforts across teams to reduce technical debt, designing from first principles.
• You will solve complex online/ production performance and evaluation issues, that require an in-depth knowledge of how the machine learning system works with systems around it.
• You will deliver codes to tie the Machine Learning solutions you develop to our ad delivery stack.
• You will mentor junior machine learning engineers and provide insightful feedback.

What Essential Skills You Will Need

• You have at least 5 years experience in the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical field
• You have a Degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• You have experience developing machine learning models at scale from inception to making a business impact.
• You have background in machine learning, control theory, and experience optimising ads serving stacks.
• You have good experience developing scalable machine learning solutions, understanding of software development life-cycle and engineering practices, experience working on a range of optimisation problems.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For at Grab

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.