{"id":349207,"url":"https://alion.io/job/parallel-member-of-technical-staff-data-engineering","title":"Member of Technical Staff, Data Engineering","company":{"id":25200,"name":"Parallel","domain":"parallel.ai","url":"https://alion.io/company/parallel","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":87,"open_postings":9,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":true}],"status":"live","first_seen_at":"2026-07-29T15:44:38Z","employer_posted_date":"2026-07-29","last_verified_at":"2026-11-06T14:10:24Z","board_verified":true,"closed_at":null,"days_open":100,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":100},"description":"About us\nParallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web.\nWe've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs. We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission.\nThe role:\nYou will build the data foundations that powers model training, product building and analytics across the company. You will work across the full lifecycle: designing pipelines that ingest and transform data at scale, building the storage and serving layers that make it fast and reliable to query, and establishing the quality, lineage, and observability systems that let others trust the data. You will anticipate scaling bottlenecks before they appear and make the architectural calls that keep the platform ahead of growing demand.\nAbout you:\nHave deep intuition on distributed data processing, data modeling, and system reliability. You like to reason about the trade-offs between batch and streaming, between storage cost and query performance, and between build-it-yourself and off-the-shelf infrastructure. You care about the data being right, not just available, and about building systems that teams across the company can depend on without a second thought.\nYou might be a good fit if you have:\nBachelor's degree or equivalent combination of education, training, and professional experience\n\nA field relevant to the role as demonstrated through coursework, training, or professional experience\n\nYears of experience required will correlated with the internal job level requirements for this position.\n\nLife at Parallel\nOur team works fully in-person, between our Palo Alto HQ and San Francisco office. We’re a flat, talent-dense organization dedicated to solving technical and creative problems.\nWe seek like-minded individuals who share our passion for applying science, creativity, and consistency to big and complex problems with equally big outcomes. These are our values:\nOwn customer impact: It’s on us to ensure real-world outcomes for our customers.\n\nObsess over craft: Perfect every detail because quality compounds.\n\nAccelerate change: Ship fast, adapt faster, and move frontier ideas into production.\n\nCreate win-wins: Creatively turn trade-offs into upside.\n\nMake high-conviction bets: Try and fail. 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