AI-generated summary
beta
This job is for a Senior Machine Learning Engineer at Grab's Mobility team. You might like this job because you'll shape urban transport with AI, predict passenger needs, and enhance user experience, all while collaborating with creative minds!
Job Description
Get to Know the Team
In the Mobility Data Science team, you are at the core of Grab's founding business. We are responsible for building the high-concurrency AI models that power the ride-hailing experience for millions of passengers. Our mission is to balance a complex two-sided marketplace, driving platform efficiency and user growth through sophisticated geospatial modeling, behavioral economics, and real-time optimization.
You will report to our Senior Data Science Manager and based onsite in our office in Petaling Jaya, Selangor.
The Critical Tasks You Will Perform
As a Senior Machine Learning Engineer in Mobility, you will build intelligent systems that predict friction, shape urban movement, and use Generative AI to redefine how passengers interact with our transport network.
Your key responsibilities are:
- Strategic Demand Shaping: Architect solutions to balance the network spatially and temporally. This involves steering demand toward higher serviceability areas or shifting passengers between service types (e.g., from JustGrab to GrabShare or Electric Vehicles) to optimize throughput.
- Demand Generation & Growth: Identify "hidden" mobility patterns—such as commuting shifts, event-based surges, or latent shopping-to-ride needs—to unlock new growth levers for the Mobility vertical.
- Preemptive Reliability & Dispute Arbitration: Predict friction before the ride even starts. You will develop models to identify high-risk bookings and cancellation probabilities, automating "who is at fault" logic to ensure fair outcomes for both passengers and drivers.
- Transformative User Experience with GAI: Leverage Generative AI (GAI) to evolve the passenger interface, making the booking and support journey more intuitive and personalized.
- Build, Iterate, and Deploy: Own the end-to-end lifecycle of ML models, translating tens of millions of passenger interactions into production-grade solutions through rigorous A/B testing and monitoring.
- Cross-Function Collaboration: Partner with product managers, engineers, analytics, design and operations teams to scale solutions.
- Innovation: Contribute to team's innovation and IP creation
Job Requirements
What Essential Skills You Will Need
- Education: Master's or Degree with minimum 3+ years of relevant experience in Machine Learning, Statistics, Computer Science, Economics, or Operations Research.
- Technical Depth: Deep understanding of ML/DL foundations, geospatial data mining, and algorithmic optimization. Proficiency in frameworks like TensorFlow or PyTorch. Proven experience in the application of Large Language Models
- Programming: Strong proficiency in Python or Scala.
- Problem Solving: Proven skill to translate complex business problems (like multi-party dispute arbitration or spatial demand shaping) into scalable math/code solutions.
- Soft Skills: Motivated self learner with communication skill proficiency to articulate technical insights to non-technical stakeholders.
Good-to-have (Preferred but not a must):
- Domain Expertise: Proven experience in ride-hailing, logistics, or large-scale marketplace platforms.
- LLM Proficiency:
- Hands-on experience building production-grade GAI use cases with fine-tuning LLMs, agentic RAG and LLM Evaluation in PRD
- Hands-on experience building autonomous agents or multi-agent systems for complex task execution.
- Systems and Scale: Experience with Spark, Flink, or Kafka for large-scale and real-time data processing.
- Engineering Excellence: Familiarity with CI/CD principles and MLOps for data science projects.
- Specialized ML: Background in recommender systems, large-scale forecasting, or combinatorial optimization.
Additional Info
Job Specialisation
Company Profile
Hiredly TSP
Upload Resume