Engineering Manager, Data & Infrastructure

Cartesia AI, Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Equity package

Health Insurance

401(k)

Commuter Allowance

Flexible PTO

Meals & Snacks

Parental Leave

Job summary

Cartesia AI, Inc. is seeking a data leader to own the systems, datasets, and research that make our training data exceptional. You will start by managing a team of 6–8 engineers and researchers and grow toward ~12 as we scale.

This role requires proven management of data infrastructure at web scale, a track record of building data for modeling projects, and a focus on data quality and collaboration with modeling teams to drive state-of-the-art multimodal AI.

Qualifications

• Proven management experience leading a data team of at least 6 people.
• Hands-on familiarity with data infrastructure at scale.
• Track record of building and leading data efforts in a modeling context.

Responsibilities

• Lead and grow a high-caliber team of data infrastructure engineers and data-focused researchers.
• Set the technical direction for how Cartesia processes, stores, and serves multimodal data at web scale.
• Drive the creation of state-of-the-art multimodal pre-training datasets and annotation models.
• Establish a rigorous, empirical approach to data quality and dataset decisions.
• Partner with modeling and research teams to translate data work into better models.
• Hire, mentor, and develop engineers and researchers as the team scales.

Skills

Data leadership

Data infrastructure

Scaling data teams

Cross-functional collaboration

Tools

Spark

Cloud storage platforms

Job description

Cartesia AI, Inc. is seeking a data leader to own the systems, datasets, and research that make our training data exceptional. You will start by managing a team of 6–8 engineers and researchers and grow toward ~12 as we scale.

This role requires proven management of data infrastructure at web scale, a track record of building data for modeling projects, and a focus on data quality and collaboration with modeling teams to drive state-of-the-art multimodal AI.