{"schemaVersion":"jobsearcher.job.v1","id":"c6cd0f5bd65da1ee8f6ddcb8","url":"https://jobsearcher.com/jobs/c6cd0f5bd65da1ee8f6ddcb8","canonicalUrl":"https://jobsearcher.com/jobs/c6cd0f5bd65da1ee8f6ddcb8","title":"ML Engineer","description":"Build feature pipelineson EMR Serverless Spark and Event streaming that turn lake data into versioned, point-in-time-correct behavioral features per user and per identity.\r\nOwn feature management:feature definitions, registry, backfills, freshness and drift monitoring, and making sure training and inference read the same features.\r\nTrain and evaluate modelsfor insider-risk scoring and anomaly detection: data prep, labeling with the analyst team, experiment tracking, and evaluation on precision, recall and alert volume.\r\nDeploy and serve modelson the Intuit ML Platform: batch inference for the daily composite score, real-time inference throughMXS(Model Execution Service) andMSaaS , and LLM-based enrichment throughLXS(LLM Execution Service).\r\nKeep scores explainable:per-feature contributions and evidence links that an analyst can follow back to source events.\r\nRun the ML lifecycle:retraining schedules, model versioning, shadow and A/B rollout against the rule-based score, monitoring, runbooks and on-call.\r\nWork with the data engineering and analytics teamsto move the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.\r\nMust-have skillsProduction data pipelines:built and operated batch and streaming pipelines with Spark/PySpark and Kafka.\r\nML training:trained, evaluated and shipped models to production (gradient boosting, anomaly detection, sequence or graph models), with sound evaluation and attention to label quality.\r\nFeature management:designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills.\r\nModel serving and MLOps:deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback.\r\nPythonas primary language (production services, PySpark, pytest), plus strongSQLand data modeling on Iceberg/Hive tables with Athena or Databricks.\r\nData analysis:profiling messy telemetry, checking signal quality, and validating score distributions against expectations#J-18808-Ljbffr","company":"Arkhya Tech","rawCompany":"arkhya tech","city":"Mountain View","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-11-04T02:10:02.990Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"ML Engineer","description":"Build feature pipelineson EMR Serverless Spark and Event streaming that turn lake data into versioned, point-in-time-correct behavioral features per user and per identity.\r\nOwn feature management:feature definitions, registry, backfills, freshness and drift monitoring, and making sure training and inference read the same features.\r\nTrain and evaluate modelsfor insider-risk scoring and anomaly detection: data prep, labeling with the analyst team, experiment tracking, and evaluation on precision, recall and alert volume.\r\nDeploy and serve modelson the Intuit ML Platform: batch inference for the daily composite score, real-time inference throughMXS(Model Execution Service) andMSaaS , and LLM-based enrichment throughLXS(LLM Execution Service).\r\nKeep scores explainable:per-feature contributions and evidence links that an analyst can follow back to source events.\r\nRun the ML lifecycle:retraining schedules, model versioning, shadow and A/B rollout against the rule-based score, monitoring, runbooks and on-call.\r\nWork with the data engineering and analytics teamsto move the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.\r\nMust-have skillsProduction data pipelines:built and operated batch and streaming pipelines with Spark/PySpark and Kafka.\r\nML training:trained, evaluated and shipped models to production (gradient boosting, anomaly detection, sequence or graph models), with sound evaluation and attention to label quality.\r\nFeature management:designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills.\r\nModel serving and MLOps:deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback.\r\nPythonas primary language (production services, PySpark, pytest), plus strongSQLand data modeling on Iceberg/Hive tables with Athena or Databricks.\r\nData analysis:profiling messy telemetry, checking signal quality, and validating score distributions against expectations#J-18808-Ljbffr","datePosted":"2026-11-04T02:10:02.990Z","dateModified":"2026-11-04T02:10:02.990Z","hiringOrganization":{"@type":"Organization","name":"Arkhya Tech","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"c6cd0f5bd65da1ee8f6ddcb8"},"url":"https://jobsearcher.com/jobs/c6cd0f5bd65da1ee8f6ddcb8"}}