Pay
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Research Engineer, Data Infrastructure (Language Modeling)
*HQ - San Francisco, CA · San Francisco, California, United States
Live, first seen by us 37 days ago- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Research — team source
Department: Research
From the employer’s posting
Before you apply
- Work authorization
- Sponsors visas
What you’ll work on
Full postingData 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.
Build and operate scalable data processing pipelines for text datasets
Design pipelines for ingestion, filtering, deduplication and augmentation
Run ablation experiments on data sources and mixture weights
Co-design data loading, versioning and experimentation pipelines with research
Source novel datasets and manage external data vendor relationships and budgets
From the employer’s posting
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.
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.
Identify and source novel datasets; manage relationships and budgets with external data vendors and partners.
What you’ll bring
All qualificationsCore experience
- 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.
- Experience with large-scale data processing using parallel infrastructure such as Ray, Spark, or Kubernetes.
- Familiarity with building and evaluating datasets for generative models and reasonable working knowledge of how they're trained and inference.
- Experience with pretraining language models.
Qualification wording
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.
Experience with large-scale data processing using parallel infrastructure such as Ray, Spark, or Kubernetes.
Familiarity with building and evaluating datasets for generative models and reasonable working knowledge of how they're trained and inference.
Experience with pretraining language models.
About Cartesia
Our mission is to architect AI that learns from and interacts with the world like humans do.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 27, 202637 days ago
- Recorded sightings
- 26
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 24, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 37
Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
Cartesia is hiring
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Databricks
- Kubernetes
- Spark
Where the posting names them
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
Nice-To-Haves - Experience with large-scale data processing using parallel infrastructure such as Ray, Spark, or Kubernetes. - Experience with pretraining language models.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job 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 RoleData 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.
Source details
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
🏢 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.