{"schemaVersion":"jobsearcher.job.v1","id":"f171c697f88daf2bb203caae","url":"https://jobsearcher.com/jobs/f171c697f88daf2bb203caae","canonicalUrl":"https://jobsearcher.com/jobs/f171c697f88daf2bb203caae","title":"Founding Machine Learning / Data Engineer","description":"About the roleRunning CI on real hardware produces a kind of data most ML teams never get to touch: power traces, sensor streams, build artifacts, console logs, test results, all tied to specific physical machines doing specific physical work. There's serious signal in there — predicting flaky boards, classifying failure modes, scoring job risk, and eventually closed-loop optimization of how the fleet itself is used.\r\nWe're hiring a founding ML / data engineer to build the pipeline that turns that data into models, and the models into product features. You'll own this end-to-end — not \"hand off a notebook and hope.\" Data ingestion, labeling, training infrastructure, evaluation, deployment, monitoring. You'll set the technical direction for ML at Primitive and shape what good looks like — from the first models in production to ML as a core part of the product.\r\nThis is the first dedicated ML hire. It's a build-from-zero role on top of a rich, real-world dataset.\r\nWhat you'll doDesign and build the data platform end-to-end: extend instrumentation where signals are missing today, then ingest from Postgres, SeaweedFS / S3, and streaming telemetry into a clean, versioned analytical layer\r\nBuild the labeling workflow that lets us (and eventually customers) label hardware events without it becoming a permanent side project\r\nDesign and operate a reproducible training stack on AWS — distributed where it needs to be, with experiment tracking, dataset versioning, and a real eval harness\r\nShip inference for product features: low-latency serving where it matters, batch scoring where it fits\r\nOperate models in production: drift monitoring, regression gates, the dashboards that tell us when a model is silently rotting\r\nPartner with the full-stack and hardware teams to integrate predictions cleanly into the product surface\r\nSet the bar for ML rigor at Primitive: eval-first development, reproducibility, honest reporting of model quality\r\nMentor new engineers on data and ML patterns as the team grows; raise the bar on data contracts, eval design, and reviewability\r\nAbout you5+ years in ML / data engineering, with at least one production ML system you took from raw data to served predictions\r\nStrong Python; comfortable with modern ML tooling (PyTorch, Hugging Face, Ray, or equivalents — we're not religious)\r\nReal opinions about data versioning, feature stores, and experiment tracking — you've used DVC, LakeFS, MLflow, or Weights & Biases and know what each is good and bad at\r\nProduction data pipeline experience with a real data warehouse — schema design, contracts, ownership\r\nAWS chops: S3, EKS-hosted training (or SageMaker), IAM that doesn't terrify the security team\r\nBuilt or operated a human-in-the-loop labeling workflow\r\nComfortable setting architectural direction for ML/data at a small company, balancing vision with pragmatism\r\nBonusTime-series, sensor, or signal-processing ML — we have a lot of it\r\nLLM fine-tuning, retrieval, or agent eval experience (there's product surface here too)\r\nBackground in hardware, EE, or anything physical — helps a lot when the data is from real machines\r\nClickHouse, dbt, Airflow / Dagster / Prefect at production scale\r\nContributed to open-source ML or data tooling\r\nStackPython, PyTorch, AWS (S3, EKS, possibly SageMaker), PostgreSQL, SeaweedFS, Grafana / Mimir. Pipeline orchestration is open (Airflow / Dagster / Prefect).\r\nHow we workOffices inNew York, NYandSan Francisco, CA— flexible in-office attendance, no fixed days per week\r\nWe like working together in person: regular team meetups across both offices\r\nSmall team, high ownership — most engineers ship to production in their first week\r\nLight on-call for serving infrastructure once models are in production\r\nBenefitsHealth, dental, and vision for you and your dependents\r\nSubstantial equity, with early exercise and an extended post-termination exercise window\r\nUnlimited paid vacation, plus local holidays\r\nEquinox membership\r\nCompensationBase salary:$180,000 – $250,000 , adjusted based on location, level, and experience. Total compensation includes substantial equity and the benefits above.#J-18808-Ljbffr","company":"Primitive","rawCompany":"primitive","city":"New York","state":"NY","isRemote":false,"isActive":true,"createdAt":"2026-11-04T02:37:22.436Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"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":"Founding Machine Learning / Data Engineer","description":"About the roleRunning CI on real hardware produces a kind of data most ML teams never get to touch: power traces, sensor streams, build artifacts, console logs, test results, all tied to specific physical machines doing specific physical work. There's serious signal in there — predicting flaky boards, classifying failure modes, scoring job risk, and eventually closed-loop optimization of how the fleet itself is used.\r\nWe're hiring a founding ML / data engineer to build the pipeline that turns that data into models, and the models into product features. You'll own this end-to-end — not \"hand off a notebook and hope.\" Data ingestion, labeling, training infrastructure, evaluation, deployment, monitoring. You'll set the technical direction for ML at Primitive and shape what good looks like — from the first models in production to ML as a core part of the product.\r\nThis is the first dedicated ML hire. It's a build-from-zero role on top of a rich, real-world dataset.\r\nWhat you'll doDesign and build the data platform end-to-end: extend instrumentation where signals are missing today, then ingest from Postgres, SeaweedFS / S3, and streaming telemetry into a clean, versioned analytical layer\r\nBuild the labeling workflow that lets us (and eventually customers) label hardware events without it becoming a permanent side project\r\nDesign and operate a reproducible training stack on AWS — distributed where it needs to be, with experiment tracking, dataset versioning, and a real eval harness\r\nShip inference for product features: low-latency serving where it matters, batch scoring where it fits\r\nOperate models in production: drift monitoring, regression gates, the dashboards that tell us when a model is silently rotting\r\nPartner with the full-stack and hardware teams to integrate predictions cleanly into the product surface\r\nSet the bar for ML rigor at Primitive: eval-first development, reproducibility, honest reporting of model quality\r\nMentor new engineers on data and ML patterns as the team grows; raise the bar on data contracts, eval design, and reviewability\r\nAbout you5+ years in ML / data engineering, with at least one production ML system you took from raw data to served predictions\r\nStrong Python; comfortable with modern ML tooling (PyTorch, Hugging Face, Ray, or equivalents — we're not religious)\r\nReal opinions about data versioning, feature stores, and experiment tracking — you've used DVC, LakeFS, MLflow, or Weights & Biases and know what each is good and bad at\r\nProduction data pipeline experience with a real data warehouse — schema design, contracts, ownership\r\nAWS chops: S3, EKS-hosted training (or SageMaker), IAM that doesn't terrify the security team\r\nBuilt or operated a human-in-the-loop labeling workflow\r\nComfortable setting architectural direction for ML/data at a small company, balancing vision with pragmatism\r\nBonusTime-series, sensor, or signal-processing ML — we have a lot of it\r\nLLM fine-tuning, retrieval, or agent eval experience (there's product surface here too)\r\nBackground in hardware, EE, or anything physical — helps a lot when the data is from real machines\r\nClickHouse, dbt, Airflow / Dagster / Prefect at production scale\r\nContributed to open-source ML or data tooling\r\nStackPython, PyTorch, AWS (S3, EKS, possibly SageMaker), PostgreSQL, SeaweedFS, Grafana / Mimir. Pipeline orchestration is open (Airflow / Dagster / Prefect).\r\nHow we workOffices inNew York, NYandSan Francisco, CA— flexible in-office attendance, no fixed days per week\r\nWe like working together in person: regular team meetups across both offices\r\nSmall team, high ownership — most engineers ship to production in their first week\r\nLight on-call for serving infrastructure once models are in production\r\nBenefitsHealth, dental, and vision for you and your dependents\r\nSubstantial equity, with early exercise and an extended post-termination exercise window\r\nUnlimited paid vacation, plus local holidays\r\nEquinox membership\r\nCompensationBase salary:$180,000 – $250,000 , adjusted based on location, level, and experience. Total compensation includes substantial equity and the benefits above.#J-18808-Ljbffr","datePosted":"2026-11-04T02:37:22.436Z","dateModified":"2026-11-04T02:37:22.436Z","hiringOrganization":{"@type":"Organization","name":"Primitive","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York","addressRegion":"NY","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f171c697f88daf2bb203caae"},"url":"https://jobsearcher.com/jobs/f171c697f88daf2bb203caae"}}