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Reflection AI

Builds AI models and deployment software

Data Acquisition Engineering Lead

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Experience building, mentoring, and growing data or infrastructure engineering teams while staying technically hands-on.
  • Deep experience building web-scale data acquisition or ingestion systems, including ownership of production-grade pipelines at multi-terabyte to petabyte scale.
  • Strong coding ability and the technical credibility to earn a team's trust.
  • Deep expertise in at least one of web crawling and acquisition, large-scale extraction and ingestion pipelines, or data lakes and corpus storage and delivery, with working knowledge across the others.
  • Fluency with distributed compute tools Ray, Beam, and Spark; orchestration tools Airflow and Prefect; formats Parquet, JSONL, and WARC; and object-store and data-lake architectures.
  • Familiarity with how large language models are trained and evaluated, and an understanding of what makes data useful for training.
  • Ability to design experiments and use proxy quality signals to guide system improvements.
  • Ability to guide strategy and drive execution across concurrent data campaigns, and to partner effectively with research teams, external data vendors, and partnerships and legal teams on acquisition.

Responsibilities

  • Build, mentor, and grow a high-performing team of data ingestion engineers, and coach and support reports in understanding and pursuing their professional growth.
  • Provide front-line leadership across web crawling and acquisition, extraction and normalization pipelines, data lakes that version and deliver training corpora, and campaigns across all three areas.
  • Become familiar with the team's technical stack and make targeted individual-contributor contributions.
  • Prioritize the team's work and run data acquisition and ingestion campaigns in a dynamic environment.
  • Guide technical and architectural decisions with an emphasis on scalability, reliability, auditability, and cost, including crawler architecture and politeness and scheduling; distributed processing with Ray, Beam, and Spark; orchestration with Airflow and Prefect; storage formats and layout with Parquet, JSONL, and WARC; object stores and lakehouse formats; deduplication; and dataset versioning and delivery.
  • Partner with pre-training and data quality teams to connect ingestion decisions to measurable downstream model impact, and run experiments evaluating crawling strategies, extraction methods, and ingestion tradeoffs.
  • Work with data partnerships, digitization operations, and external vendors to onboard new sources within legal, licensing, and robots.txt constraints.
  • Raise standards for technical judgment, prioritization, communication, and execution.

About the Company

Reflection AI

Builds AI models and deployment software

Reflection AI develops language models for software developers, companies and governments that want control over how AI is deployed and adapted. Its research concentrates on coding, reasoning and AI agents that work through tasks with software tools. The lab builds the surrounding software and infrastructure as well as models, letting customers run AI through its hosted platform or in their own computing environments. Its commercial work pairs model access with engineers who design applications and support customized enterprise deployments from development through operation. Reflection AI was founded in 2024 and is headquartered in New York City.

Company size
201-500
Company stage
Late Stage VC
Total funding
$4.6B
Headquarters
New York City, New York
Founded
2024

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