Grab seeks a Senior Machine Learning Engineer to join our Mobility Data Science team. You will build intelligent systems that predict friction, shape urban movement, and use Generative AI to redefine how passengers interact with our transport network. You'll translate tens of millions of passenger interactions into production-grade solutions.
What You'll Do
- Architect solutions to balance the network spatially and temporally, steering demand toward higher serviceability areas or shifting passengers between service types to optimize throughput.
- Identify 'hidden' mobility patterns such as commuting shifts, event-based surges, or latent shopping-to-ride needs to unlock new growth levers.
- Develop models to predict friction before the ride starts, identifying high-risk bookings and cancellation probabilities, and automating 'who is at fault' logic for fair outcomes.
- Leverage Generative AI (GAI) to evolve the passenger interface, making the booking and support journey more intuitive and personalized.
- Own the end-to-end lifecycle of ML models through rigorous A/B testing and monitoring.
- Partner with product managers, engineers, analytics, design and operations teams to scale solutions.
- Contribute to team’s innovation and IP creation.
What We're Looking For
- Ph.D. graduate, or Master’s Degree with 3+ years of relevant experience in Machine Learning, Statistics, Computer Science, Economics, or Operations Research.
- 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.
- Strong proficiency in Python or Scala.
- Proven ability to translate complex business problems into scalable math/code solutions.
- Self-motivated learner with excellent communication skills to articulate technical insights to non-technical stakeholders.
Nice to Have
- Proven experience in ride-hailing, logistics, or large-scale marketplace platforms.
- 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.
- Experience with Spark, Flink, or Kafka for large-scale and real-time data processing.
- Familiarity with CI/CD principles and MLOps for data science projects.
- Background in recommender systems, large-scale forecasting, or combinatorial optimization.
Technical Stack
- Languages: Python, Scala
- Frameworks: TensorFlow, PyTorch
- Data Processing: Spark, Flink, Kafka
Team & Environment
You will join the Mobility Data Science team, responsible for building high-concurrency AI models for ride-hailing.
Benefits & Compensation
- Term Life Insurance
- Comprehensive Medical Insurance
- GrabFlex benefits package
- Parental and Birthday leave
- Love-all-Serve-all (LASA) volunteering leave
- Confidential Grabber Assistance Programme
- FlexWork arrangements such as differentiated hours
We are committed to building an inclusive and equitable workplace that provides equal opportunity for all Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
