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全职 Senior Machine Learning Engineer - Trust Platform 工作, 薪水, Grab 公司招聘中 - Ricebowl

Senior Machine Learning Engineer - Trust Platform

Singapore

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工作地点

  • Singapore Singapore Singapore

职位描述

岗位职责

 

Know your Role

As a Senior Machine Learning Engineer, you will solve trust problems end to end. You will not limit itself to training models. You will investigate complex risk patterns and determine the simplest solution. Additionally, you will build the required data and production components. Finally, you will measure whether the solution works in the real world.

 

You will

  • Investigate fraud, abuse, account integrity, and platform-safety problems using data and domain knowledge.
  • Define success metrics while balancing detection quality, user experience, latency, and operational cost.
  • Determine whether a problem is best solved using ML, heuristics, rules, graph techniques, optimisation, or system changes.
  • Build data pipelines and features from high-volume batch and streaming data.
  • Develop proof-of-concept solutions and validate them against realistic baselines.
  • 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 to assess effectiveness, false positives, drift, and latest attack patterns.
  • Perform root-cause analysis and adapt solutions as adversarial behaviours evolve.
  • Improve the technical foundations that make future trust solutions faster and safer to deliver.

 

You will need

  • At least 4 years of relevant experience in software development and problem solving
  • Proficiency in SQL and at least one production programming language.
  • Working knowledge of machine learning and statistical evaluation.
  • Experience using data to solve open-ended product or operational problems.
  • Experience building production data pipelines, backend services, or decision systems.
  • Ability to articulate technical reasoning, assumptions, trade-offs, and experimental results.
  • Comfortable operating across modelling, data engineering, and software engineering boundaries.

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