Senior Machine Learning Engineer - Trust Platform

Grab; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

Grab invites experienced software professionals to join our risk and trust team in Singapore. You will investigate fraud and platform-safety issues using data, build scalable data pipelines, and determine the right mix of ML, heuristics, and system changes.

You will convert prototypes into production-ready systems and integrate them into real-time and offline risk workflows. You will design experiments, monitor performance including false positives and drift, and clearly communicate technical

Qualifications

• Hold at least four years of relevant software development and problem-solving experience.
• Demonstrate proficiency in SQL and at least one production programming language.
• Apply working knowledge of machine learning and statistical evaluation methods.
• Use data to solve open-ended product or operational problems in prior roles.
• Build production data pipelines, backend services, or decision systems with demonstrated experience.
• Articulate technical reasoning, assumptions, trade-offs, and experimental results clearly to stakeholders.
• Operate comfortably across modelling, data engineering, and software engineering boundaries daily.

Responsibilities

• Investigate fraud, abuse, account integrity, and platform-safety problems using data.
• Define success metrics balancing detection quality, user experience, latency, and operational cost.
• Determine whether problems suit ML, heuristics, rules, graph techniques, optimization, or system changes.
• Build data pipelines and features from high-volume batch and streaming data sources.
• Develop proof-of-concept solutions and validate them against realistic baseline comparisons.
• Convert successful prototypes into scalable, tested, and observable production systems.
• Integrate solutions into real-time risk decisioning and offline detection workflows.
• Design experiments and monitoring for effectiveness, false positives, drift, and attack patterns.
• Perform root-cause analysis and adapt solutions as adversarial behaviors evolve over time.
• Improve technical foundations that make future trust solutions faster and safer to deliver.

Skills

SQL

Python

Machine Learning

Data Analysis

Job description

What you'd do:

• Investigate fraud, abuse, account integrity, and platform-safety problems using data.
• Define success metrics balancing detection quality, user experience, latency, and operational cost.
• Determine whether problems suit ML, heuristics, rules, graph techniques, optimization, or system changes.
• Build data pipelines and features from high-volume batch and streaming data sources.
• Develop proof-of-concept solutions and validate them against realistic baseline comparisons.
• Convert successful prototypes into scalable, tested, and observable production systems.
• Integrate solutions into real-time risk decisioning and offline detection workflows.
• Design experiments and monitoring for effectiveness, false positives, drift, and attack patterns.
• Perform root-cause analysis and adapt solutions as adversarial behaviors evolve over time.
• Improve technical foundations that make future trust solutions faster and safer to deliver.

What they want:

• Hold at least four years of relevant software development and problem-solving experience.
• Demonstrate proficiency in SQL and at least one production programming language.
• Apply working knowledge of machine learning and statistical evaluation methods.
• Use data to solve open-ended product or operational problems in prior roles.
• Build production data pipelines, backend services, or decision systems with demonstrated experience.
• Articulate technical reasoning, assumptions, trade-offs, and experimental results clearly to stakeholders.
• Operate comfortably across modelling, data engineering, and software engineering boundaries daily.