New opportunity

Lead Product Manager, Physical AI

Grab · Singapore

About this role

Role Purpose: Grab is seeking a Lead Product Manager for its Physical AI initiative. You will own the platform from pilot to scale, building a system for embodied and egocentric AI training data across Southeast Asia by connecting labs with field collectors. Responsibilities: You will set the platform strategy, including demand, field collection, and data curation; define partner priorities and unit economics; and own the collector experience, including tooling and incentives. You will translate AI lab needs into dataset specifications, partner with Engineering and Data Science to improve workflows, and manage cross-border delivery, legal, and privacy compliance. Qualifications: Requires at least 4 years of product management experience, including shipping AI-native products and building multi-sided platforms. You must demonstrate commercial judgment, the ability to manage complex trade-offs, and experience working across Engineering, Data Science, and legal teams. Preferred qualifications include experience in embodied AI, robotics, world models, geospatial data, or large-scale field operations. Benefits: Grab offers comprehensive medical and term life insurance, GrabFlex benefits, parental and birthday leave, volunteering leave, a confidential assistance program, and flexible work arrangements.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementAI-native product developmentMulti-sided platform strategyData strategyTraining dataAI evaluationFeedback loopsCommercial judgmentUnit economicsCross-functional leadershipData privacyOperations management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Platform Strategy and Unit Economics
You must define the end-to-end strategy for a multi-sided platform connecting AI labs with field data collectors. This requires balancing lab demand with supply-side incentives, determining build-versus-partner trade-offs, and ensuring the platform's unit economics are sustainable as it scales from pilot to regional operations.
Data Quality and Workflow Integration
You are responsible for translating complex AI lab requirements into precise dataset specifications. This involves partnering with Engineering and Data Science to build feedback loops that turn model performance data into actionable improvements for field collection and annotation workflows.
Cross-Border Compliance and Operations
You must navigate the legal and privacy complexities of collecting and delivering data across multiple Southeast Asian countries. This requires close collaboration with Legal, Data Privacy, and local operations teams to ensure all field activities and data handling meet regional regulatory standards before scaling.
Collector Experience and Tooling
You own the end-to-end experience for field collectors, which is critical for maintaining a reliable supply of high-quality data. This includes designing intuitive field tooling, clear instructional frameworks, and incentive structures that keep the workforce productive and engaged.

Likely challenges to think through

Balancing Technical Rigor with Field Realities
AI labs often require highly specific data formats that may be difficult to capture consistently in diverse field environments. Prepare to discuss how you manage the trade-off between strict technical specifications and the practical limitations of field operations, including how you prioritize quality assurance without stalling throughput.
Managing Multi-Sided Platform Ambiguity
Building a platform that connects disparate groups—AI researchers and field collectors—is inherently complex. Prepare to demonstrate how you have previously aligned conflicting stakeholder incentives and managed the roadmap for a product where the 'customer' (the lab) and the 'supplier' (the collector) have vastly different needs.
Scaling Compliance Across Jurisdictions
Scaling a data collection platform across Southeast Asia introduces significant regulatory variance. Prepare to discuss your experience in managing cross-border data privacy and legal risks, specifically how you build compliance into the product design phase rather than treating it as an afterthought.

Practice your answers

Technical practice

Designing Multi-Sided Platform Incentives

What to coverExplain how you would balance the needs of AI labs (demand) with field collectors (supply) to ensure a sustainable ecosystem. Focus on how you would structure incentive models and unit economics to maintain high-quality data collection while managing operational costs.

Translating AI Lab Requirements into Data Specifications

What to coverDescribe your process for converting abstract research needs into concrete dataset specifications. Discuss how you define quality criteria and feedback loops to ensure the data collected in the field directly improves model performance.

Managing Data Privacy and Compliance at Scale

What to coverDetail your approach to navigating cross-border data privacy regulations when collecting egocentric or geospatial data. Address how you would partner with legal and privacy teams to implement consent frameworks and secure data handling procedures.

Optimizing Field Collection Workflows

What to coverDiscuss how you would design tooling for field collectors to ensure data consistency and reliability. Suggest how you would use AI-assisted tools to streamline annotation and quality assurance processes.

Evaluating Build-Versus-Partner Trade-offs

What to coverExplain your framework for deciding whether to build internal collection capabilities or partner with third-party vendors. Focus on how you evaluate long-term strategic value versus short-term speed-to-market.

Scaling from Pilot to Production

What to coverDescribe the key metrics and milestones you would use to determine if a pilot is ready for regional scaling. Address how you would manage risks related to field readiness and operational consistency across different countries.

Defining AI-Native Product Success

What to coverExplain how you measure the success of an AI-native product where the value is derived from training data quality. Discuss the importance of evaluation metrics and how you iterate based on model performance feedback.

Handling Geospatial and Egocentric Data Challenges

What to coverAddress the unique technical challenges of collecting and curating egocentric or geospatial data. Suggest how you would ensure data diversity and mitigate bias in the training sets.

Behavioural practice

Navigating Cross-Functional Conflict

What to coverDescribe a situation where you had to align competing priorities between Engineering, Data Science, and Operations teams. Focus on how you communicated the trade-offs to reach a consensus that supported the overall product strategy.

Managing Ambiguity in Product Strategy

What to coverProvide an example of a time you owned a product from an ambiguous opportunity to a launched state. Explain how you defined the initial scope and how you adapted your roadmap as you learned from early feedback.

Influencing Stakeholders Without Direct Authority

What to coverShare an experience where you had to influence country-level stakeholders or external partners to adopt a new process or standard. Explain how you built trust and ensured compliance with your platform requirements.

Learning from Product Failure

What to coverDescribe a time when a product feature or strategy did not perform as expected. Explain how you analyzed the failure, what you learned about the user or market, and how you adjusted your approach for the next iteration.

Sharpen your knowledge

Topics to prepare

Multi-sided Platform Economics
Review frameworks for balancing supply and demand in marketplaces. Exercise: Map out the unit economics for a hypothetical data collection platform, identifying the key levers for collector incentives versus lab data quality requirements.
AI-Native Product Lifecycle
Study the transition from pilot to scale for data-centric AI products. Exercise: Draft a strategy document outlining how to scale a pilot data collection project across multiple countries while maintaining consistent quality and legal compliance.
Data Privacy and Cross-Border Compliance
Understand the regulatory challenges of moving sensitive training data across borders. Exercise: Create a checklist for evaluating the legal and privacy risks associated with collecting egocentric or geospatial data in diverse regulatory environments.

Skills to revise

Defining Dataset Specifications
Practice translating abstract AI model requirements into concrete, actionable instructions for field collectors. Exercise: Write a sample specification document for a sensor-based data collection task, including quality assurance criteria and edge-case handling.
Cross-Functional Trade-off Analysis
Develop the ability to prioritize competing needs from Engineering, Legal, and Operations. Exercise: Simulate a scenario where a legal constraint impacts the collection timeline and propose three distinct options to stakeholders, highlighting the trade-offs for each.
Feedback Loop Design
Learn to integrate model performance metrics back into operational workflows. Exercise: Design a feedback loop that uses model evaluation results to automatically trigger updates to field collector training materials or collection tooling.

Your preparation checklist

Analyze Multi-Sided Platform Economics
Prepare a framework for balancing supply (field collectors) and demand (AI labs). Be ready to discuss how you would design incentive structures, manage unit economics, and prioritize partner needs in a marketplace where data quality is the primary product.
Draft an AI Data Lifecycle Strategy
Create a brief document outlining how you would translate high-level AI model requirements into concrete dataset specifications. Include how you would build feedback loops between Data Science teams and field operations to ensure continuous quality improvement.
Prepare a Cross-Functional Compliance Case Study
Select a past project where you navigated legal, privacy, or cross-border regulatory constraints. Outline the specific trade-offs you made between speed-to-market and compliance, and how you communicated these risks to stakeholders.
Develop Field Tooling and Collector Experience Requirements
Outline the essential features for a mobile tool used by field collectors to capture egocentric or geospatial data. Focus on how to ensure data consistency, ease of use, and reliable payment/incentive tracking for a distributed workforce.
Formulate Strategic Questions for the Hiring Team
Prepare questions regarding the current maturity of the Physical AI platform, the biggest bottlenecks in the existing data collection pipeline, and how the team currently balances the build-versus-partner decision for new collection technologies.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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