{"schemaVersion":"jobsearcher.job.v1","id":"eefa85d2864ebcdeb5443810","url":"https://jobsearcher.com/jobs/eefa85d2864ebcdeb5443810","canonicalUrl":"https://jobsearcher.com/jobs/eefa85d2864ebcdeb5443810","title":"ML Engineer","description":"What you'll doBuild feature pipelines on EMR Serverless Spark and Event streaming 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 the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.Must-have skillsProduction 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","company":"Arkhya Tech","rawCompany":"arkhya tech","city":"Mountain View","state":"HI","isRemote":false,"isActive":false,"createdAt":"2026-10-29T07:37:52.135Z","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":"What you'll doBuild feature pipelines on EMR Serverless Spark and Event streaming 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 the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.Must-have skillsProduction 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","datePosted":"2026-10-29T07:37:52.135Z","dateModified":"2026-10-29T07:37:52.135Z","hiringOrganization":{"@type":"Organization","name":"Arkhya Tech","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"HI","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"eefa85d2864ebcdeb5443810"},"url":"https://jobsearcher.com/jobs/eefa85d2864ebcdeb5443810"}}