Tech Lead Manager, Data Infrastructure

Ovii's Interpretation of the Role

We need a Tech Lead Manager to own Cartesia's multimodal data strategy and build the high‑throughput pipelines that feed our foundation models. You will lead a data‑engineering team, partner with research and inference groups, and ensure data quality at scale.

Role Snapshot

• Lead and grow a data‑engineering team
• Define multimodal data strategy for pre‑ and post‑training
• Design scalable pipelines for text, audio, and video
• Collaborate with research and inference engineers
• Set and enforce data‑quality standards
• Manage external data‑vendor relationships and budgets

Must-Have Requirements

• ML data infrastructure (training pipelines, dataset versioning, large‑scale loading)
• Audio‑centric multimodal data processing
• High‑throughput distributed data pipelines
• Modern software engineering practices
• Team leadership & engineering management
• ML data infrastructure
• multimodal audio data
• engineering team leadership

Nice-to-Have Signals

• Familiarity with building/evaluating datasets for generative models
• generative model dataset evaluation

Work Setup

• Location: San Francisco, United States
• Work mode: ONSITE
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: yes

Not Specified in JD

• Salary range
• Equity details
• Remote eligibility
• Education requirement
• Certifications
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Craft and execute a multi‑modal data roadmap covering human, synthetic, and web‑scale sources
• Guide a team of data engineers in building and operating ingestion, preprocessing, augmentation, versioning, and loading pipelines
• Co‑design data systems alongside model training and serving infrastructure to ensure GPU‑aware loading and efficient evaluation
• Implement rigorous data‑quality metrics and feedback loops that directly influence model behavior
• Source novel datasets, negotiate with external vendors, and oversee related budgeting
• Partner with research scientists to align data pipelines with experimental goals

Good Fit If You Have

• Proven track record leading high‑impact engineering teams in research‑driven settings
• Hands‑on experience building ML‑focused data pipelines at scale
• Deep familiarity with audio data formats, preprocessing, and streaming patterns