---
title: Research Engineer Data Infrastructure at Cartesia
description: Cartesia is hiring for the Research Engineer Data Infrastructure role in San Francisco,
  CA. See the full description and apply.
type: job
url: https://www.foundrole.com/jobs/research-engineer-data-infrastructure-at-cartesia-01a0d47d-e3e3-7e26-bf4c-a6ac92e7ffba
date: 2026-11-15T01:26:44Z
og_description: Join Cartesia as Research Engineer Data Infrastructure in San Francisco, CA. Pays
  $200K–$350K per year. Full-time, on-site role.
og_image: https://www.foundrole.com/og/mtrc54.png
breadcrumbs:
  - label: Home
    url: https://www.foundrole.com/
  - label: Search
    url: https://www.foundrole.com/jobs
---

| | |
|---|---|
| **Company** | Cartesia |
| **Location** | San Francisco, CA |
| **Salary** | $200K/yr - $350K/yr |
| **Type** | Full Time |
| **Posted** | Sep 24, 2026 |

## Is this job real?

**Ghost job check:** A- · Low risk. Probably a real opening.

## Visa sponsorship

- **H-1B:** Cartesia sponsors H-1B visas. It filed 17 H-1B petitions (FY2026 Q1 – FY2026 Q4).
- **E-Verify:** No E-Verify enrollment on record for Cartesia.

## Pay for similar jobs

Most similar jobs pay $201,000 – $321,000 a year (median $243,000).

## Description

# **About Cartesia**

Our mission is to architect AI that learns from and interacts with the world like humans do.

We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.

We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.

# About the Role

Data is the lifeblood of our models, and we are looking for a **Research Engineer, Data Infrastructure** to build the datasets and systems that power pretraining at Cartesia. In this role, you will write performant, scalable infrastructure to acquire, process, and curate massive datasets, and partner closely with research to optimize the characteristics and composition of data mixtures. Your work will directly shape the capabilities and quality of our foundational models.

## Your Impact

- Build and operate performant, scalable data processing infrastructure for acquiring, ingesting, and combining massive text datasets.

- Design and operate scalable, high-throughput, and reproducible data pipelines — covering ingestion, preprocessing, filtering, deduplication, and augmentation.

- Design and run ablation experiments to understand how data sources, processing choices, and mixture weights affect model quality.

- Partner closely with research and infrastructure teams to co-design data loading, versioning, and experimentation pipelines.

- Establish and enforce rigorous standards for data quality, with a tight feedback loop between dataset characteristics and model behavior.

- Identify and source novel datasets; manage relationships and budgets with external data vendors and partners.

## What You Bring

- Hands-on experience with ML data infrastructure: training data pipelines, dataset versioning, large-scale data loading, and the interplay between data systems and model training and inference.

- Strong modern engineering execution: clean, well-tested code, fluency with current tools, and a willingness to pick the right tool for the problem rather than defaulting to familiar patterns.

- Familiarity with building and evaluating datasets for generative models and reasonable working knowledge of how they're trained and inference.

## Nice-To-Haves

- Experience with large-scale data processing using parallel infrastructure such as Ray, Spark, or Kubernetes.

- Experience with pretraining language models.

*Note: Cartesia participates in E-Verify and will provide the federal government with Form I-9 information to confirm employment eligibility after hire.*

# **More Details**

🏢 **In-office policy:** We’re an in-person team based out of offices in 🇺🇸 San Francisco, 🇬🇧 London and 🇮🇳 Bangalore. We love being in the office, hanging out together, and learning from each other every day.

**🌎 Visa sponsorship:** We provide visa sponsorship support and assess each circumstance on a case-by-case basis. However, visa sponsorship is dependent on many factors, including the role you are applying for, and the location you are going to be based, and so we can't always guarantee success. Your Recruiter will work with you to understand your visa sponsorship needs from the first call.

🚢 **We ship fast.** All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way.

🤝 **We support each other.** We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed.

# **Our Benefits (US Employees Only)**

💰 **Compensation** Competitive base salary alongside attractive equity package.

🩺 **Health Insurance** Fully covered medical insurance along with dental and vision for you and your family.

🧑‍🧑‍🧒‍🧒 **Parental Leave** 9 weeks paternity & 12 weeks maternity leave

🏦 **401(k)**

🚆 **Commuter Allowance** A monthly stipend to help you get to and from the office.

🏖️ **Flexible PTO** Take as much time as you need to recharge your batteries.

🍲 **Meals & Snacks** Lunch, dinner and plenty of snacks, provided daily.

🦖 **Your own personal Yoshi**

## **Our Commitment to Equal Opportunity**

*Cartesia is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other legally protected status.*
## Skills

- Data Preprocessing
- Data Ingestion
- Machine Learning Model Training
- Data Annotation
- High-Throughput Screening
- Co-Design
- Big Data
- Apache Spark
- Kubernetes
- Software Versioning
- Ablation
- Data Loading
- Large Language Models (Llm)
- Data Quality
- Machine Learning
- Generative AI Foundations
- Ray (Distributed Computing)
- Data Processing
- Budgeting
- Model Pretraining
- Data Pipeline
## Benefits

- Health Insurance
- Parental Leave
- Paid Time Off (Pto)
- 401(k) Plans
- Employee Stock Options (ESOs)

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