{"schemaVersion":"jobsearcher.job.v1","id":"7ba893f744b7f18baa2069ff","url":"https://jobsearcher.com/jobs/7ba893f744b7f18baa2069ff","canonicalUrl":"https://jobsearcher.com/jobs/7ba893f744b7f18baa2069ff","title":"AI Data Engineer","description":"Fluency is enabling the autonomous Enterprise. (in person)\r\nYou're needed to build the data infrastructure that powers enterprise intelligence. We're not wiring up dashboards. We're building pipelines that ingest, process, and structure the raw signals of how work actually happens, at a scale nobody has attempted.\r\nFluency is looking for an AI Data Engineer to design and build the data systems that feed our process conformance, productivity measurement, and AI impact analysis across Fortune 500 organisations.\r\nThe Problem Space\r\nYou'll be building data infrastructure that handles messy, real-world signals: screenshots, OCR text, application metadata, and behavioural events. The challenge is transforming unstructured chaos into reliable, queryable data that our ML systems can consume, at scale, with cost constraints that make naive approaches untenable.\r\nThis means\r\nDesigning ingestion pipelines that process millions of screenshots and behavioural events daily\r\nBuilding data validation and quality systems that catch drift before it corrupts models\r\nCreating feature stores and serving infrastructure that balance freshness against compute cost\r\nOptimising storage and query patterns for time-series behavioural data\r\nOrchestrating complex DAGs that coordinate OCR, LLM enrichment, and downstream aggregations\r\nThe playbook doesn't exist. You'll write it.\r\nWe're backed by T1 VCs like Accel and are hitting an inflection point with Enterprises all around the globe.\r\nYou'll work directly with founders and our engineering team on technical challenges that span data engineering, LLM pipelines, and production systems.\r\nAbout the Role\r\nWe're looking for someone with:\r\nStrong Python fundamentals and software engineering discipline\r\nExperience building production data pipelines (Dagster, Airflow, Prefect, or similar)\r\nData modelling expertise: designing schemas for analytical and ML workloads\r\nInfrastructure experience: AWS (S3, RDS, Glue, Lambda), containerisation, IaC\r\nProduction database experience: PostgreSQL, graph databases (Neo4j, Neptune, or similar)\r\nMonitoring and observability for data systems: lineage, quality metrics, alerting\r\nComfort with ambiguity and novel problem domains\r\nComputer Science Background, with caveat. If you don't have a CS background, you're challenged to beat one of the founders in a 1:1 whiteboard duel on DS&A judged by Hung. Neither founder has a formal CS background, but come prepped.\r\nThere will be an expectation to stay up to business context, which could involve:\r\nWatching key customer calls\r\nInteracting with customers\r\nHelping with product thinking\r\nStrongly Preferred\r\nExperience with LLM pipelines and model serving infrastructure\r\nShipping models to production: deployment, versioning, monitoring\r\nOCR, document processing, or image pipeline experience\r\nCost optimisation for data-intensive systems\r\nFamiliarity with dbt, Spark, or similar transformation frameworks\r\nExperience with multi-region data architectures and residency requirements\r\nYou've operated data systems at scale under real constraints\r\nInteresting personal projects that demonstrate depth\r\nOur Customers\r\nWe work with some of the world's largest:\r\nFinancial services enterprises (Aon)\r\nManufacturing enterprises (Misumi)\r\nAnd many more across the enterprise spectrum (PVH)\r\nOur Culture\r\nYou're expected to be in love with the craft. You're expected to like laughing. You're expected to want to work on novel problems. You're expected to find satisfaction in novelty. You're expected to solve under obscurity.\r\nOur Values\r\nIn hesitation lies destruction; in action, glory.\r\nThose who merely meet expectations abandon the pursuit of greatness.\r\nOne who dwells within the forum must regard it as hallowed ground.\r\nOne who has not tasted the grapes declares them sour.\r\nOne who sits alone at the feast misses the richness of the table.\r\nLocation\r\nFull-time, in-person role based in San Francisco, CA.\r\nWe offer E3 sponsorship for Australians to relocate with stipend\r\nCompensation\r\nUS$150K - $250K salary, depending on candidate and experience\r\nSubstantial equity, every offer includes ownership\r\nMac, Linux, or Windows, your call\r\nHigh-impact work with global enterprises\r\nTechnical, product-led founders\r\nDon't apply if:\r\nYou want hybrid or remote\r\nYou don't like working hard and with insane velocity\r\nYou want to work a 9 to 5\r\nYou're not comfortable with rapid iteration\r\nYou think data engineering is plumbing work\r\nYou've never operated production pipelines\r\nYou don't have personal projects\r\nYou dislike constraints (we have them: cost, latency, reliability tradeoffs are real)\r\nYou aren't ambitious\r\nYou don't have a good reason for wanting to work at an early-stage company\r\nHiring Process\r\nResume screen\r\n1:1 with founder\r\nTechnical deep-dive on past data engineering work\r\nWork through a real problem with the team\r\nOffer\r\nWe strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products, see value #5.\r\nJ-18808-Ljbffr","company":"SupportFinity","rawCompany":"supportfinity","city":"Millbrae","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-06-25T00:58:35.349Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"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":"AI Data Engineer","description":"Fluency is enabling the autonomous Enterprise. (in person)\r\nYou're needed to build the data infrastructure that powers enterprise intelligence. We're not wiring up dashboards. We're building pipelines that ingest, process, and structure the raw signals of how work actually happens, at a scale nobody has attempted.\r\nFluency is looking for an AI Data Engineer to design and build the data systems that feed our process conformance, productivity measurement, and AI impact analysis across Fortune 500 organisations.\r\nThe Problem Space\r\nYou'll be building data infrastructure that handles messy, real-world signals: screenshots, OCR text, application metadata, and behavioural events. The challenge is transforming unstructured chaos into reliable, queryable data that our ML systems can consume, at scale, with cost constraints that make naive approaches untenable.\r\nThis means\r\nDesigning ingestion pipelines that process millions of screenshots and behavioural events daily\r\nBuilding data validation and quality systems that catch drift before it corrupts models\r\nCreating feature stores and serving infrastructure that balance freshness against compute cost\r\nOptimising storage and query patterns for time-series behavioural data\r\nOrchestrating complex DAGs that coordinate OCR, LLM enrichment, and downstream aggregations\r\nThe playbook doesn't exist. You'll write it.\r\nWe're backed by T1 VCs like Accel and are hitting an inflection point with Enterprises all around the globe.\r\nYou'll work directly with founders and our engineering team on technical challenges that span data engineering, LLM pipelines, and production systems.\r\nAbout the Role\r\nWe're looking for someone with:\r\nStrong Python fundamentals and software engineering discipline\r\nExperience building production data pipelines (Dagster, Airflow, Prefect, or similar)\r\nData modelling expertise: designing schemas for analytical and ML workloads\r\nInfrastructure experience: AWS (S3, RDS, Glue, Lambda), containerisation, IaC\r\nProduction database experience: PostgreSQL, graph databases (Neo4j, Neptune, or similar)\r\nMonitoring and observability for data systems: lineage, quality metrics, alerting\r\nComfort with ambiguity and novel problem domains\r\nComputer Science Background, with caveat. If you don't have a CS background, you're challenged to beat one of the founders in a 1:1 whiteboard duel on DS&A judged by Hung. Neither founder has a formal CS background, but come prepped.\r\nThere will be an expectation to stay up to business context, which could involve:\r\nWatching key customer calls\r\nInteracting with customers\r\nHelping with product thinking\r\nStrongly Preferred\r\nExperience with LLM pipelines and model serving infrastructure\r\nShipping models to production: deployment, versioning, monitoring\r\nOCR, document processing, or image pipeline experience\r\nCost optimisation for data-intensive systems\r\nFamiliarity with dbt, Spark, or similar transformation frameworks\r\nExperience with multi-region data architectures and residency requirements\r\nYou've operated data systems at scale under real constraints\r\nInteresting personal projects that demonstrate depth\r\nOur Customers\r\nWe work with some of the world's largest:\r\nFinancial services enterprises (Aon)\r\nManufacturing enterprises (Misumi)\r\nAnd many more across the enterprise spectrum (PVH)\r\nOur Culture\r\nYou're expected to be in love with the craft. You're expected to like laughing. You're expected to want to work on novel problems. You're expected to find satisfaction in novelty. You're expected to solve under obscurity.\r\nOur Values\r\nIn hesitation lies destruction; in action, glory.\r\nThose who merely meet expectations abandon the pursuit of greatness.\r\nOne who dwells within the forum must regard it as hallowed ground.\r\nOne who has not tasted the grapes declares them sour.\r\nOne who sits alone at the feast misses the richness of the table.\r\nLocation\r\nFull-time, in-person role based in San Francisco, CA.\r\nWe offer E3 sponsorship for Australians to relocate with stipend\r\nCompensation\r\nUS$150K - $250K salary, depending on candidate and experience\r\nSubstantial equity, every offer includes ownership\r\nMac, Linux, or Windows, your call\r\nHigh-impact work with global enterprises\r\nTechnical, product-led founders\r\nDon't apply if:\r\nYou want hybrid or remote\r\nYou don't like working hard and with insane velocity\r\nYou want to work a 9 to 5\r\nYou're not comfortable with rapid iteration\r\nYou think data engineering is plumbing work\r\nYou've never operated production pipelines\r\nYou don't have personal projects\r\nYou dislike constraints (we have them: cost, latency, reliability tradeoffs are real)\r\nYou aren't ambitious\r\nYou don't have a good reason for wanting to work at an early-stage company\r\nHiring Process\r\nResume screen\r\n1:1 with founder\r\nTechnical deep-dive on past data engineering work\r\nWork through a real problem with the team\r\nOffer\r\nWe strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products, see value #5.\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T00:58:35.349Z","dateModified":"2026-06-25T00:58:35.349Z","hiringOrganization":{"@type":"Organization","name":"SupportFinity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7ba893f744b7f18baa2069ff"},"url":"https://jobsearcher.com/jobs/7ba893f744b7f18baa2069ff"}}