Machine Learning Engineer

Machine Learning Engineer: Mountain View CA (Backfill) EX-INTUIT STRONGLY PREFERRED OR SOMEONE VERY GOOD LOCAL TO BAY AREA CA What you'll do Build feature pipelines on BPP (EMR Serverless Spark) and SPP/Event Bus that turn lake data into versioned, point-in-time-correct behavioral features per user and per identity.

Own feature management: feature definitions, registry, backfills, freshness and drift monitoring, and making sure training and inference read the same features.

Train and evaluate models for insider-risk scoring and anomaly detection: data prep, labeling with the analyst team, experiment tracking, and evaluation on precision, recall and alert volume.

Deploy and serve models on the Intuit ML Platform: batch inference for the daily composite score, real-time inference through MXS (Model Execution Service) and MSaaS, and LLM-based enrichment through LXS (LLM Execution Service).

Keep scores explainable: per-feature contributions and evidence links that an analyst can follow back to source events. Run the ML lifecycle: retraining schedules, model versioning, shadow and A/B rollout against the rule-based score, monitoring, runbooks and on-call.

Work with the data engineering and analytics teams to move RETNA from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.

Must-have skills Production data pipelines: built and operated batch and streaming pipelines with Spark/PySpark and Kafka. Intuit BPP and SPP/Event Bus experience preferred.

ML training: trained, evaluated and shipped models to production (gradient boosting, anomaly detection, sequence or graph models), with sound evaluation and attention to label quality.

Feature management: designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills. Model serving and MLOps: deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback.

Python as primary language (production services, PySpark, pytest), plus strong SQL and data modeling on Iceberg/Hive tables with Athena or Databricks.

Data analysis: profiling messy telemetry, checking signal quality, and validating score distributions against expectations.Build feature pipelines on BPP (EMR Serverless Spark) and SPP/Event Bus that turn lake data into versioned, point-in-time-correct behavioral features per user and per identity.

Own feature management: feature definitions, registry, backfills, freshness and drift monitoring, and making sure training and inference read the same features.

Train and evaluate models for insider-risk scoring and anomaly detection: data prep, labeling with the analyst team, experiment tracking, and evaluation on precision, recall and alert volume.

Deploy and serve models on the Intuit ML Platform: batch inference for the daily composite score, real-time inference through MXS (Model Execution Service) and MSaaS, and LLM-based enrichment through LXS (LLM Execution Service).

Keep scores explainable: per-feature contributions and evidence links that an analyst can follow back to source events. Run the ML lifecycle: retraining schedules, model versioning, shadow and A/B rollout against the rule-based score, monitoring, runbooks and on-call.

Work with the data engineering and analytics teams to move RETNA from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.

Must-have skills Production data pipelines: built and operated batch and streaming pipelines with Spark/PySpark and Kafka. ML training: trained, evaluated and shipped models to production (gradient boosting, anomaly detection, sequence or graph models), with sound evaluation and attention to label quality.

Feature management: designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills. Model serving and MLOps: deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback.

Python as primary language (production services, PySpark, pytest), plus strong SQL and data modeling on Iceberg/Hive tables with Athena or Databricks. Data analysis: profiling messy telemetry, checking signal quality, and validating score distributions against expectations.