Perplexity
Member of Technical Staff (Software Engineer, Data Flywheel)
Vaga remota de AI Research & Systems com fit claro de localização do candidato.
Publicada13 de abr. de 2026
Países elegíveis4 países aceitos
Sinal de senioridadeLead
Modelo de trabalhoRemoto
Locais aceitos para candidatos
AlemanhaSérviaReino UnidoEstados Unidos
Resumo da vaga
Member of Technical Staff (Software Engineer, Data Flywheel)
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Responsibilities
- Build the systems and pipelines that enable Search, Product, and other teams to independently access and utilize reliable eval verdicts without bottlenecks
- Take ownership of the "evals-to-product" loop, autonomously determining the best way to turn raw signals into durable datasets that power decision-making across the company
- Build a robust simulator pipeline capable of replaying user interactions with the product in formats legible to LLMs and VLMs, reflecting product changes as they are shipped
- Maintain data trust by implementing monitoring, lineage, and quality checks, ensuring downstream consumers can rely on the results implicitly
- Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality
Qualifications
- 3+ years of software engineering experience shipping production systems
- Strong proficiency in Python and SQL with the ability to write production-grade, maintainable code
- Experience with big data systems including distributed compute and large-scale storage
- Solid fundamentals in data modeling, system design, and debugging distributed systems
- Experience with AWS and lakehouse ecosystems like Databricks or Spark
- Comfortable with agentic coding workflows and using AI-assisted development tools to iterate faster
Preferred Qualifications
- Data engineering background including pipelines, orchestration, and warehousing patterns
- Familiarity with LLM/VLM interfaces, tokenization, structured formats, and multimodal payloads
- Experience with evaluation platforms, experimentation systems, or machine learning infrastructure
- Prior work supporting customer-facing products at scale
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