# Member of Technical Staff

**Company:** [Perplexity](https://hotfix.jobs/companies/perplexity)
**Location:** San Francisco, CA, Palo Alto, CA
**Role:** Data Engineering
**Salary:** $200k – $350k/yr
**Experience:** 3+ years
**Skills:** Python, SQL, AWS, Databricks, Spark, Data Modeling, System Design, Distributed Systems, Data Pipelines, Data Orchestration, Data Warehousing, LLMs, Vlms, Machine Learning Infrastructure, Tokenization
**Posted:** 2026-04-13

> Build production systems and data pipelines that turn evaluation signals into durable datasets, replayable product simulations, and trusted verdicts for Search and Product teams. The role requires 3+ years of software engineering experience, Python and SQL proficiency, distributed data systems expertise, and AWS or lakehouse experience.

## Job Description

## Responsibilities
- Build systems and pipelines that enable Search, Product, and other teams to independently access and use reliable evaluation verdicts without bottlenecks.
- Own the **evals-to-product** loop, determining how to turn raw signals into durable datasets that power company-wide decision-making.
- Build a robust simulator pipeline that replays user interactions with the product in formats legible to LLMs and VLMs, reflecting product changes as they ship.
- Implement monitoring, lineage, and quality checks to maintain data trust and ensure downstream consumers can rely on results.
- Work on a small, high-impact team shaping how Answer Quality is measured and improved.

## Requirements
- 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.
- Strong fundamentals in data modeling, system design, and debugging distributed systems.
- Experience with **AWS** and lakehouse ecosystems such as **Databricks** or **Spark**.
- Comfortable with agentic coding workflows and AI-assisted development tools.

## Nice-to-haves
- 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.
- Experience supporting customer-facing products at scale.

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