Research Engineer, Data Infrastructure (Language Modeling)
cartesia
OnSite
*HQ - San Francisco, CA
Data
Uncategorized
Job Score
80 pts
On-site model (+70)
Data (+10)
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.
What did you think of this job?
Loading comments...
About Data
The Data field has undergone a radical transformation with the rise of Generative AI. Data professionals are fundamental for evidence-based decision-making across all industries.
Key specializations include Data Engineering, Data Science, Business Intelligence, Machine Learning Engineering, and Analytics. Tools like SQL, Python, Spark, dbt, and cloud platforms (AWS, GCP, Azure) are essential.
The data market continues with high demand and salaries among the most competitive in the technology sector, with many remote work opportunities.
Discover Other Areas
Understand the scope of work, key skills, and tools used in different career areas.
About Project Manager
The Project Manager is the professional responsible for planning, executing, and controlling projects end-to-end, ensuring they are delivered on time, within budget, and with the expected quality. With the growing complexity of businesses, project management professionals are fundamental to organizational success.
Key skills include planning and scheduling, scope, cost, risk, quality, and resource management, stakeholder communication, cross-functional team leadership, and use of agile and traditional methodologies. Certifications like PMP, PRINCE2, and Six Sigma are important differentiators.
Project Managers in technology companies are highly valued, especially those who master agile methodologies (Scrum, Kanban), tools like Jira and MS Project, and can deliver complex projects efficiently. The field offers opportunities from project analyst to head of PMO, with a focus on execution, governance, and business value.
About Talent Acquisition
Talent Acquisition is the strategic area responsible for attracting, selecting, and hiring the best professionals for the organization. Unlike traditional recruitment, TA acts as a strategic business partner, aligning talent acquisition with the company's long-term objectives.
Key skills include advanced sourcing, employer branding, labor market analysis, talent pipeline management, and candidate experience. Tools like LinkedIn Recruiter, ATS (Greenhouse, Lever, Ashby), and assessment platforms are essential.
TA professionals in technology companies are highly valued, especially those who master tech sourcing, workforce planning, and recruitment metrics like time-to-hire and cost-per-hire.
About SEO Analyst
The SEO Analyst is the professional responsible for optimizing websites for search engines, increasing organic visibility and qualified traffic. With the growing importance of digital marketing, SEO professionals are fundamental to any online presence strategy.
Key skills include on-page and off-page SEO, technical SEO, keyword research, SEO audits, link building, optimized content creation, and metrics analysis. Tools like Google Search Console, Google Analytics, Ahrefs, Semrush, Moz, and Screaming Frog are essential for daily work.
SEO analysts in technology companies are highly valued, especially those who master technical SEO, Core Web Vitals, and data-driven content strategies. The field offers opportunities from junior analyst to head of SEO, with a focus on organic growth, domain authority, and return on investment.
About Software Development
Software Development is one of the most dynamic and constantly evolving fields in the job market. Professionals in this area are responsible for creating, maintaining, and optimizing web, mobile, and desktop applications that impact millions of users daily.
Key languages and frameworks include JavaScript (React, Node.js, Vue.js), Python (Django, Flask), Java (Spring), PHP (Laravel), and TypeScript. Demand for full-stack developers continues to grow, especially in tech companies and startups.
Salaries range from entry-level to senior positions, with growing opportunities for remote work and international freelancing.
About Engineering
Software Engineering goes beyond traditional development, focusing on scalability, performance, and system architecture. Software engineers are responsible for designing infrastructures that support millions of simultaneous users.
Skills include microservices architecture, DevOps, cloud computing, application security, and performance optimization. Knowledge of containerization (Docker, Kubernetes) and CI/CD is increasingly required.
Senior software engineers are rare and highly compensated professionals, with opportunities at major global tech companies.
Comments 0