About the role

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users. As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements. Responsibilities - Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions - Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures - Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data - Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements - Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality Qualifications - 4+ years of software, data, or machine learning engineering experience shipping and operating production systems - Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems - Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines - Demonstrated ownership of ambiguous technical projects from initial design through production operation - Ability to work effectively with data scientists, engineers, and product partners Preferred Qualifications - Experience building evaluation, experimentation, observability, or machine learning infrastructure - Familiarity with LLM and agent systems, including tool use, execution traces, replay, and simulation - Experience building on top of large-scale data processing platforms such as Databricks, Snowflake, or ClickHouse

Requirements

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

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

Member of Technical Staff (Answer Quality & Evals)

About the role

Requirements

Responsibilities

Skills

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