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Parallel is a web infrastructure company that enables leading businesses to build AI agents with programmatic access to the web. The company has raised $230M from top-tier VCs (Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, Terrain) and is valued at $2B.
As a Member of Technical Staff in Data Engineering, you will own the data foundations powering model training, product development, and analytics across the organization. Your responsibilities span the full data lifecycle: designing and implementing pipelines that ingest and transform data at scale, building storage and serving layers optimized for fast and reliable querying, and establishing data quality, lineage, and observability systems that enable company-wide trust in data assets.
You will anticipate scaling bottlenecks before they materialize and make architectural decisions that keep the platform ahead of growing demand. This is a high-impact individual contributor role requiring deep expertise in distributed data processing, data modeling, and system reliability. You'll reason through critical trade-offs: batch vs. streaming architectures, storage cost vs. query performance, and build-vs-buy infrastructure decisions. You care deeply about data correctness and building systems that teams across the company can depend on without hesitation.
Parallel operates a flat, talent-dense organization working fully in-person between Palo Alto HQ and San Francisco offices. The company values owning customer impact, obsessing over craft, accelerating change, creating win-wins, and making high-conviction bets. Compensation includes competitive salary, generous equity, visa sponsorship, 401K, daily lunch and office snacks, dinner at the office, unlimited vacation, and Caltrain pass reimbursement.
