Senior Machine Learning Engineer (Simulation) at Kudo

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

02 Aug, 26

Salary

0.0

Posted On

04 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Reinforcement Learning, Simulation, Scala, Python, Golang, Microservices, Functional Programming, TensorFlow, PyTorch, Spark, Ray, Distributed Systems, Marketplace Optimization, Data Processing

Industry

Software Development

Description

Company Description 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. Job Description Get to know the Team The Fulfilment Tech family is one of the pillars allowing Grab to out-serve our consumers and partners in different businesses and marketplaces across Southeast Asia. We are working on high-throughput, real-time distributed systems that use machine-learning techniques to solve hundreds of millions of requests per day. Our mission is to offer the best products and experiences to our driver partners to increase the adoption and engagement of our services. Improve driver partner opportunities and efficiency to fulfil consumer orders without fail, rain or shine. And to create efficient marketplaces by determining a price that is both sustainable and loved by our partners and consumers. Grab is Southeast Asia's leading super-app. We provide everyday services such as deliveries, mobility, financial services, enterprise services and others to millions of users across the region. At the fulfilment machine engineering team, we are trying to solve challenging problems in the marketplace that involve dynamic pricing, supply and demand management. We are looking for senior machine learning engineers to join the team to help us make that vision a reality by developing and refining cutting-edge reinforcement learning models and simulation platforms. Get to know the Role Reporting into the Senior Engineering Manager and based in Grab Singapore One North office, this is a hands-on role involving building large-scale simulation platforms. You will build a digital twin of Grab's marketplace that consists of tens of thousands of consumers, drivers, and merchants. Furthermore, you will develop reinforcement learning, optimization and control models to solve business problems inside Grab's marketplace and deploy them at scale. The Critical Tasks You Will Perform Architect and develop our simulation platform to simulate the response from the marketplace that involves different types of services that Grab is providing Work with data scientists and engineers to design simulation flow that would support their Platform Policy designs and optimisations Collaborate with data scientists and engineers to design simulation SDKs to improve the user experience of the simulation platform Work with Data Scientists to integrate and build feedback loops to run policy search using the simulation platform Design and scale the simulation platform to run hundreds and thousands of simulations per day Contribute to service capacity and demand planning, software performance analysis, costing, tuning and optimization. Participate in code and design reviews to maintain our high development standards. Qualifications What Essential Skills You Will Need To perform the tasks above, you will need: At least 2 years building production machine learning systems that serve live traffic and require you to handle model deployment, monitoring, and maintenance At least 2 years developing large-scale simulation environments modelling complex systems with multiple working entities (e.g., marketplaces, logistics networks, or agent-based systems) Professional-level coding in Scala and Python to build the simulation platform core and ML model pipelines Professional-level coding in Golang to develop microservices that handle real-time marketplace data streams Experience with functional programming frameworks (such as Cats Effect or similar) to build concurrent, non-blocking systems that handle high-throughput data processing Experience with at least one ML framework (TensorFlow or PyTorch) to implement reinforcement learning models for marketplace optimization Experience with distributed data processing frameworks (such as Spark or Ray) to grow simulations across compute clusters and process large-scale training data 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 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.

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Responsibilities

Architect and develop a large-scale simulation platform to model marketplace dynamics including consumers, drivers, and merchants. Collaborate with data scientists to design reinforcement learning models and integrate feedback loops for policy optimization.