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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.

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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 Infrastructure and DevOps

Infrastructure and DevOps are responsible for creating, maintaining, and optimizing IT environments that support applications at scale. This area is fundamental for system reliability and performance.

Key technologies include AWS, GCP, Azure, Docker, Kubernetes, Terraform, Ansible, CI/CD (GitHub Actions, GitLab CI, Jenkins), and monitoring (Datadog, Grafana, Prometheus).

DevOps engineers and SREs are highly sought-after professionals, with salaries among the highest in the technology sector.

About Ecommerce Analyst

The Ecommerce Analyst is the professional responsible for analyzing online sales data, buyer behavior, and virtual store performance to guide strategic decisions. They combine data analysis with ecommerce knowledge to optimize conversion, average order value, and return on investment.

Key skills include Google Analytics (GA4), Hotjar, conversion funnel analysis, cohort analysis, customer segmentation, pricing analysis, and ecommerce metrics (CAC, CLV, AOV, conversion rate). Knowledge of SQL, Power BI, Google Tag Manager, and platforms like Shopify and VTEX is a differentiator.

Ecommerce Analysts in technology companies are highly valued, especially those who can turn buyer behavior data into actionable insights to increase revenue and reduce cart abandonment. The field offers opportunities from junior analyst to ecommerce analytics manager.

About IT Governance

IT Governance is the area responsible for ensuring that information technology resources are used strategically, efficiently, and in compliance with standards and regulations. IT governance professionals ensure that technology supports business objectives in a secure and reliable manner.

Key skills include IT service management (ITIL), IT audit and compliance, risk management, business continuity, disaster recovery, metrics and indicators (SLAs, KPIs), and strategic alignment between IT and business. Frameworks like COBIT, ITIL, ISO 27001, and compliance standards are essential.

IT Governance professionals in technology companies are highly valued, especially those who master ITSM, IT audit, and risk management. The field offers opportunities from governance analyst to CIO/CTO, with a focus on efficiency, compliance, security, and business value.

About Business Intelligence

Business Intelligence (BI) is the area responsible for transforming raw data into strategic information for decision-making. BI professionals build dashboards, reports, and analyses that help companies understand their performance and identify growth opportunities.

Key skills include data modeling (star schema, snowflake), ETL (extraction, transformation, loading), advanced SQL, BI tools (Power BI, Tableau, Looker), data warehousing, KPIs, and business metrics analysis (MRR, churn, cohort). Knowledge of dbt, Airflow, and data pipelines is a differentiator.

BI professionals in technology companies are highly valued, especially those who master data visualization, analytics engineering, and can translate complex data into actionable insights for the business. The field offers opportunities from BI analyst to head of data, with a focus on data-driven decision making.

About QA and Testing

QA and Software Testing are fundamental to ensure the quality and reliability of applications. QA professionals ensure that the delivered product meets requirements and is free of critical defects.

Key skills include manual and automated testing, Selenium, Cypress, Playwright, Postman, JMeter, and CI/CD pipeline knowledge. Performance and security testing are differentiators.

With the adoption of DevOps and continuous deployment, the demand for automation QAs and SDETs continues to grow.

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Administration Career Guide

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Data Career Guide

Data Science, Data Engineering, BI, Machine Learning, and AI. From training to the job market.

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Product Career Guide

Product Management, Product Ownership, Agile, Scrum, and OKRs. From strategy to execution.

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Tech & Remote Glossary

Stop getting lost in interviews and job descriptions

The job market, especially within tech and global companies, has developed its own dialect. Not understanding these acronyms can make you lose valuable opportunities or poorly negotiate your contract. To end this problem, we created the Definitive Glossary for the Remote Professional.

🏢 Work Models & Routine

Async (Asynchronous Work)
A communication model where responses don't need to be immediate. Instead of back-to-back meetings, the team relies on well-structured documents, threads, and messages. It's the gold standard for global companies spanning multiple time zones.
Sync (Synchronous Work)
The opposite of Async. It requires the team to be online and available at the same time for meetings, live chats, and real-time collaboration.
Daily / Stand-up
A quick daily meeting (usually 15 minutes) common in Agile (Scrum) methodologies. The team answers three questions: What did I do yesterday? What will I do today? Are there any blockers?
All-Hands / Town Hall
A company-wide meeting involving all employees. Usually led by the founders (C-Levels) to present results, new goals, and answer team questions.
1:1 (One-on-One)
A recurring individual meeting between a professional and their direct manager. It is used for career alignment, feedback, and problem-solving, not just for project status updates.

💰 Contracts, Benefits & Compensation

PTO (Paid Time Off)
Instead of strict, categorized leave policies, modern US companies usually offer a flexible pool of days (e.g., 20 days of PTO, or even "Unlimited PTO") that you can use for vacations, sick days, or personal matters, while receiving your regular compensation.
Equity / Stock Options
Company ownership. The startup offers you the right to buy shares at a heavily discounted strike price in the future. If the company grows, goes public, or is acquired, these shares can be highly lucrative.
Vesting (Vesting Schedule)
The rule that controls your Equity. It usually lasts 4 years. You don't get all the shares on day one; you "earn" them gradually as you stay with the company. A "1-year Cliff" means you must stay for at least one year to receive your first batch of shares.
RSUs (Restricted Stock Units)
Unlike Stock Options (where you have the right to buy the stock), RSUs are actual shares the company grants you as a bonus or part of your compensation package, following a strict Vesting schedule.
Independent Contractor (1099 / B2B)
The most common international hiring model for global talent working for US companies. You act as a service provider (business-to-business), receiving the gross salary (often six-figure compensation) without standard local payroll tax deductions at the source.

🤖 Recruitment & Hiring Process

ATS (Applicant Tracking System)
The "robot" that reads your resume. Software like Greenhouse, Ashby, and Workday are used to filter candidates by keywords before a human even looks at the document. (Pro tip: this is why your resume must be clean, semantic, and have the right keywords).
JD (Job Description)
The document that lists the responsibilities, technical requirements, and benefits of the open position.
Cultural Fit
The interview stage that evaluates if your core values, communication style, and worldview align with the company's culture. This is the ultimate test of your Soft Skills.
Onboarding
The integration process. It's the period of your first few weeks at the company, where you get your access credentials, learn about the culture, study the internal documentation, and understand how the product works.

🚀 How to use this to your advantage?

The secret isn't just knowing what these acronyms mean, but using them actively. If during an interview for a premium tech role you ask, "How does your PTO policy and Vesting schedule work?", the recruiter will immediately perceive you as a high-level professional, familiar with the global market standards.

The remote job market requires preparation. And having the right vocabulary is the first big step to securing six-figure proposals and standing out among thousands of applicants.

Expert Tip

Why the Data Era Has Never Been More Profitable

Discover how BI professionals are shaping the corporate future, driving strategic decision-making, and landing the best global remote jobs in the market.

Reading time: 5 minutes | Category: Data / BI

The Data Market Explosion

A decade ago, oil was considered the world's most valuable resource; today, that title indisputably belongs to data. However, raw data without interpretation is just noise. It is exactly at this turning point that the Business Intelligence (BI) market exploded in demand and financial valuation.

Companies of all sizes, from agile startups to global unicorns, have realized they can no longer base their decisions on guesswork. The BI professional has become the architect of financial and operational predictability. According to Gartner, in its annual reports on the Magic Quadrant for Analytics and BI, adopting business intelligence platforms is no longer a competitive advantage, but a matter of corporate survival.

The Required Profile: Way Beyond the Dashboard

The biggest mistake professionals make when trying to transition into Business Intelligence is believing the job comes down to creating pretty visual panels (Dashboards). Today's market, especially international companies paying six-figure salaries, requires a deep analytical mindset combined with solid technical skills.

Tech Recruiters are configuring their ATS systems (like Greenhouse and Ashby) to filter candidates who master the following verticals:

  • Data Modeling and ETL: The ability to Extract, Transform, and Load data from multiple sources into a Data Warehouse or Data Lake.
  • Query Languages: Advanced proficiency in SQL remains the backbone of any data operation.
  • Advanced Visualization: Tools like Microsoft Power BI (with deep knowledge in DAX), Tableau, and Looker are mandatory requirements.
  • Critical Thinking & Business Acumen: As highlighted by the Harvard Business Review, data professionals must translate technical complexity into clear answers for C-Level executives.

Compensation and the Global Job Route (US Remote)

The Business Intelligence market is perfectly adaptable to the Remote Worldwide model. Because the work essentially deals with cloud infrastructure and logical analysis, geographical barriers have been completely eliminated.

"The ability to analyze massive volumes of data and extract actionable insights is the most valued skill in the new digital economy. The global market is literally buying intelligence."

For global professionals, this represents a golden opportunity. North American and European startups are actively seeking Senior BI talent to work remotely, offering compensation in strong currencies (USD and EUR) through B2B contracts. Base salaries that easily surpass the $80,000 to $120,000+ range (six-figure compensation) are a common reality for Mid-level and Senior analysts who have fluent English and a robust analytical portfolio.

How to Stand Out in the Technical Interview

To break through the technical interview barrier for premium BI positions, candidates must prepare a problem-oriented portfolio. Instead of presenting a generic sales dashboard, showcase a documented case study. Show the data source, how the SQL was structured for cleansing, which predictive or statistical models were applied, and, most importantly, what was the financial impact (ROI) of that analysis.