Senior Machine Learning Engineer (Reinforcement Learning)

Get to Know the Team

At Grab we are dedicated to enhancing lives with our super-app offering services such as deliveries mobility and financial solutions to millions across Southeast Asia. As a member of the AI Automation Team youll play a pivotal role in creating automated ML/AI solutions to address complex challenges within our marketplace. Our team focuses on advancing experiments building embeddings creating intelligent recommendations and optimising large-scale marketplaces. We are in search of experienced machine learning engineers eager to drive innovation and refine our experimentation platforms.

Get to Know the Role

You will report into Senior Machine Learning Engineering Manager and work onsite at Grab One North Singapore office. Youll be hands-on in developing large-scale machine learning reinforcement learning and optimization solutions. Your expertise will help create pioneering data-driven technologies impacting millions of consumers drivers and merchants within Grabs marketplace. You will engage in troubleshooting and resolving issues as part of on-call rotations.

The Critical Tasks You Will Perform

• You will design and develop scalable reinforcement learning frameworks to address personalised targeting and marketplace optimization issues using noisy real-world data.
• You will devise offline reinforcement learning strategies using historical data to improve decision-making processes.
• You will construct multi-agent systems aimed at optimising marketplace dynamics and interactions.
• You will implement safe exploration methods for online learning to ensure responsible policy shifts.
• You will craft evaluation frameworks for assessing reinforcement learning policies and their outcomes.
• You will develop monitoring systems to oversee the performance of deployed reinforcement learning agents.
• You will participate in code and design reviews promoting high-standard engineering practices.

Qualifications :

What Essential Skills You Will Need

• You have a degree in computer science machine learning or a similar field that underpins your technical expertise in developing machine learning solutions.
• You bring at least 5 years of specialized experience in machine learning focusing on reinforcement learning and optimization.
• You have knowledge of modern reinforcement learning frameworks such as RLlib and Stable Baselines. You can deploy reinforcement learning algorithms at scale.
• You have programming skills in Python coupled with sound software engineering practices.
• You have hands-on experience developing and evaluating simulation environments crucial for policy evaluation and strategy testing.
• You are adept in distributed computing and optimization essential for handling large-scale data.
• You have experience with ML deployment platforms and MLOps ensuring streamlined machine learning operations.

Additional Information :

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 lifes challenges.
• Balancing personal commitments and lifes 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.

Remote Work :

No

Employment Type :

Full-time