{"id":1904034,"url":"https://alion.io/job/success-matcher-data-technical-product-manager","title":"Data Technical Product Manager","company":{"id":3864750,"name":"Success Matcher","domain":"successmatcher.com","url":"https://alion.io/company/success-matcher","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"Product","role_family":"Product","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":171000,"max_usd":334000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":644},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Computer Vision","optional":true},{"name":"Multimodal AI","optional":true}],"status":"live","first_seen_at":"2026-10-05T12:11:54Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-12T07:08:49Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"About the Opportunity\nOur client, a well-funded, early-stage robotics and AI company is seeking a highly technical and execution-oriented Data Technical Product Manager (TPM) to own the end-to-end data infrastructure powering next-generation machine learning systems.\nThis role sits at the intersection of machine learning, data engineering, operations, and product management. You will be responsible for building and scaling the data flywheel that transforms raw data collected from deployed robotic systems into high-quality training datasets used to improve AI performance.\nThe ideal candidate has experience managing large-scale data operations, working closely with ML teams, and translating ambiguous technical requirements into structured execution plans.\nThis is a unique opportunity to join a rapidly growing robotics company building cutting-edge autonomous systems while working directly alongside world-class engineers, researchers, and founders.\nWhat You'll Do\nOwn the End-to-End Data Platform\nDrive the roadmap for the central data platform.\nManage the complete data lifecycle from capture through ingestion, storage, labeling, curation, and delivery to ML training pipelines.\nPartner with engineering teams to scale infrastructure supporting large-volume datasets.\n\nPartner with Machine Learning Teams\nTranslate ML research requirements into actionable data collection and annotation specifications.\nDefine dataset requirements and collection strategies to support model development.\nEnsure researchers have access to reliable, high-quality training data.\n\nDefine Data Quality Standards\nCreate QA frameworks, audit processes, and validation workflows.\nEstablish standards for data quality, coverage, consistency, and labeling accuracy.\nIdentify sensor drift, data degradation, and annotation issues before they impact training outcomes.\nManage Data Annotation Operations\nSource and manage third-party labeling vendors.\nDefine vendor performance expectations and quality metrics.\nConduct audits and implement continuous improvement initiatives.\nBuild Data Discovery & Infrastructure Capabilities\nPartner with infrastructure and platform engineers to improve:\nData ingestion\nCataloging\nSearch\nVersioning\nDataset management\n\nDesign the Data Flywheel\nBuild systems that automatically surface edge cases and production failures.\nCreate workflows that capture and route valuable operational data back into future training cycles.\nImprove data collection efficiency and model iteration speed.\n\nDrive Metrics & Operational Excellence\nDefine and monitor critical metrics including:\nThroughput\nLabel quality\nData coverage\nDataset freshness\nDrift detection\nOperational efficiency\n\nRequired Qualifications\n4+ years of experience in one or more of the following:\nTechnical Product Management\nData Engineering\nLarge-Scale Data Operations\nProven experience building and managing end-to-end data pipelines.\nExperience supporting applied machine learning or AI systems.\nStrong understanding of data quality management and governance.\nAbility to operate effectively in highly ambiguous, fast-moving startup environments.\nExperience coordinating across engineering, research, operations, and external vendors.\n\nPreferred Qualifications\nExperience with multimodal datasets including:\nVideo\nSensor data\nPoint clouds\nTelemetry\nRobotics, autonomous systems, computer vision, or industrial AI experience.\nFamiliarity with large-scale data annotation workflows.\nExperience designing feedback loops that improve model performance over time.","description_format":"text","description_chars":3530,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics"],"lifecycle":[{"event":"open","at":"2026-10-05T12:11:54Z"}],"visa":[],"liveness":{"score":54,"band":"ok","label":"Likely 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