Our Mission

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.

About The Role

Reflection's Data team builds the training corpora our frontier models learn from. Before a model can learn anything, the data has to be found, fetched, extracted, and delivered reliably, responsibly, and at enormous scale. The ingestion layer is the machinery that turns the open web, licensed corpora, and other large-scale sources into well-structured, versioned, auditable datasets for pre-training.

As Data Ingestion Lead, you'll provide front-line leadership of the team that builds this layer, spanning all three of its pillars: web crawl, data ingestion pipelines, and data lakes. You'll build, mentor, and grow a team of data ingestion engineers, guide the technical and architectural decisions across crawling, extraction, and corpus storage/delivery, and work closely with the pre-training research, data quality, and data partnerships teams that depend on what you ship. You'll stay close enough to the stack to make targeted contributions as an individual contributor and to maintain a deep understanding of the team's technical work. Subtle decisions at the ingestion layer—what we crawl, how we extract, what we keep—ripple through training and directly affect where our models are strong, safe, and where they fail.

What You'll Do

• Build, mentor, and grow a high-performing team of data ingestion engineers. Coach and support your reports in understanding, and pursuing, their professional growth.
• Provide front-line leadership across the full ingestion stack: web crawling and acquisition, extraction and normalization pipelines, and the data lakes that version and deliver training corpora and the campaigns that run across all three.
• Stay hands-on: become familiar with the team's technical stack enough to make targeted contributions as an individual contributor.
• Manage day-to-day execution: prioritize the team's work and run data acquisition and ingestion campaigns in a highly dynamic, fast-paced environment.
• Guide technical and architectural decisions, emphasizing scalability, reliability, auditability, and cost—crawler architecture and politeness/scheduling, distributed processing (Ray/Beam/Spark), orchestration (Airflow/Prefect), storage formats and layout (Parquet, JSONL, WARC; object stores and lakehouse formats), deduplication, and dataset versioning and delivery.
• Close the loop with research: partner with pre-training and data quality teams to tie ingestion decisions to measurable downstream model impact, and run experiments to evaluate crawling strategies, extraction methods, and ingestion tradeoffs.
• Work across the wider data effort—data partnerships, digitization operations, and external vendors—to onboard new sources within legal, licensing, and robots.txt constraints.
• Raise the bar for technical judgment, prioritization, communication, and execution in a fast-moving environment.