USAMember of Technical Staff (Data Platform)
2 мес. назад
165k–250k USD / yearUSALeadOnsite
data platformdata engineeringpythonsql
Member of Technical Staff responsible for data platform development at AI technology company.
Обязанности
- Our data infrastructure needs to be built, from scratch. We’re migrating off a custom in-house pipeline that can’t keep up with our growth, and the decisions made now will shape how product, growth, and ML operate for years. As our first Data Engineer, you’ll own the architecture, build the pipelines, and lay the foundation everything else runs on.
- You’ll sit in engineering, report to the CTO, and work directly with the co-founders. This is a 0→1 build with full ownership.
Будет плюсом
- Experience with high-volume event data from consumer, social, or UGC platforms
- Background supporting ML feature pipelines or training data infrastructure
- Prior experience as the first or only data engineer at a startup
- Familiarity with monetization or ads data infrastructure
- Experience with dbt, Airflow, or Dagster
Условия
- Competitive base salary plus equity
- Health, dental, and vision coverage
- Lunch provided daily
- Join us to help build the future of interactive entertainment.
Другое
- is an AI technology company building super-intelligence for the interactive world, delivered through a breakout consumer platform.
- Today's frontier models can write, reason, and generate video. None can build a world you can step into and act on. Interactive world models, systems that generate playable experiences, are one of the defining problems on the path to ASI, and the foundation of the next great consumer platform. Interactivity is the holy grail of content: not something you watch, but something that responds to you. Cracking it will redefine how the world plays, learns, and connects. We're further ahead than anyone: the only team building the intelligence and the platform together, and both are already live.
- Our social UGC platform lets anyone create and play interactive experiences through natural language. And it's one of the fastest-growing consumer products in the world, with over 30 million users in just months since launch. Creators bring to life games, companions that remember and grow with them, learning experiences that adapt like a great tutor, and social worlds built with friends. The platform also powers our research with data no video dataset contains: millions of creators prompting and refining gives us paired data of human intent and the worlds that fulfill it, and every play session produces ground-truth, action-labeled interaction data at scale. All of it flows back into our models, and research ships to real players in days, spinning a flywheel no one else can build.
- We are a small team from Google Research, Meta, FAIR,, and Stanford, led alongside Chief Scientist Fei-Fei Li, with frontier-scale compute and direct ownership of core model problems. We are backed by Sequoia Capital alongside Google, NVIDIA, LG, SEA Gaming, Dentsu Ventures, and Conviction, and by luminaries including Eric Schmidt and Jerry Yang.
- The next foundational AI company will be built where world models meet a billion players. We're the rare place where frontier research and a breakout consumer platform are one company, and where your work ships to millions, not to a paper. Come build it with us.
- Build and maintain scalable pipelines for high-volume user and game event data across ingestion, transformation, and serving
- Design the data models and warehouse structure that product, growth, and future ML systems depend on
- Write and optimize SQL queries - profiling, tuning, and setting the performance standard
- Own data quality from day one: monitoring, alerting, and anomaly detection
- Build toward a self-serve analytics layer that non-engineers can trust and use independently
- Instrument systems deeply: structured logging, performance profiling, latency awareness across the stack
- Have 4-7 years of data engineering experience, including at least one 0→1 build or major pipeline rebuild
- Have migrated or replaced production pipelines without dropping data or breaking downstream consumers
- Understand latency tradeoffs - you know when milliseconds matter and when daily batches are fine
- Have enough analytical range to serve product and growth teams directly, not just build pipes they can’t use
- Are self-directed and comfortable making architectural decisions without a complete spec