Senior Machine Learning Engineer - Trust Platform

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.

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