Member of Technical Staff (RecSys)
At a glance
Senior Software Engineering role at Astrocade. Palo Alto · full-time · $200,000–$300,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Build the system that selects games for each player, from finding candidates through ordering recommendations
- Create the data flow for recommendations, including feature work, training examples and fast delivery
- Develop personalization that responds to players’ behavior, preferences and context over time
- Set up quality measures and experiments, then use results to improve recommendations
- Add instrumentation across the recommendation system so the team can track its behavior
What you bring
- At least four years working in recommendation systems, ML engineering or applied ML for consumer products
- You have built ranking or personalization from start to finish, such as feeds, video or gaming
- You have evaluated recommendations through both offline measures and online testing
- You can work across data pipelines, feature stores, model training and serving
Who this fits
This senior, founding role suits someone ready to make architectural calls and build a recommendation system from the ground up, with broad ownership. You’ll report to the CTO and work directly with the co-founders. The position is onsite in Palo Alto.
From the employer
Astrocade 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 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. 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, Character.ai, and Stanford, led alongside Fei-Fei Li, with frontier-scale compute and direct ownership of core model problems. We are backed by Sequoia Capital, Google, NVIDIA, and 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.
About the Role
Our recommendation system doesn't exist yet, and it needs to. We have a large and fast-growing catalog of games, millions of users, and new content being created every day. What you show someone, and when, is the difference between a session that lasts two minutes and one that lasts two hours. As our RecSys founding member, you'll own this problem end-to-end - set the architecture, build the foundation, and grow it from rule-based systems to deep learning. The decisions made now will shape how discovery works on the platform for years.
You'll report to the CTO, and work directly with the co-founders. This is a 0 to 1 build with full ownership.
What You'll Do
Design and build the systems that decide which games surface to which players, from candidate retrieval through final ranking
Own the full data pipeline - ingestion, feature engineering, training data construction, and low-latency serving
Build personalization systems and models that adapt to user behavior, preferences, and context over time
Build eval infrastructure to measure recommendation quality: offline metrics, online experiments, and business outcomes
Run A/B tests and translate results into concrete system improvements
Instrument the recommendation stack deeply so the team can move fast with confidence
You'd Be a Great Fit If You:
Have 4-7+ years of experience in recommendation systems, ML engineering, or applied ML in a consumer context
Have built ranking or personalization systems end-to-end - feeds, video, gaming, or similar
Experience running recommendation evals end-to-end (offline + online)
Understand the full stack - data pipelines, feature stores, model training, and serving
Are comfortable making architectural decisions on a greenfield system without much scaffolding
Are self-directed and energized by ownership, not just execution
Bonus Points
Experience at companies with large-scale consumer recommendation systems (YouTube, Netflix, TikTok, Instagram, LinkedIn, Twitter/X)
Familiarity with both rule-based and deep learning approaches, and when to use each
Background in UGC or creator platforms where content is high-volume and fast-changing
What we Offer
Competitive salary, meaningful equity, and performance bonus.
100% employer-paid medical, dental, and vision insurance for employees and eligible dependents.
Flexible PTO and unlimited sick time.
$650/month AI Tools budget to use on the tools that help you do your best work.
$500/month Lifestyle Benefit for wellness, fitness, meals, house cleaning, and more.
$200/month Fun Benefit to spend on hobbies, travel, games, concerts, or whatever brings you joy.
Up to 16 weeks paid parental leave for birth parents and 12 weeks for non-birth parents, plus a $1,500/month childcare benefit.
$2,000 workspace setup allowance and $200/month commuter benefit.
Daily breakfast, lunch, and dinner on in-office days, plus regular team events and offsites.
401(k) retirement plan through Human Interest.
Join us to help build the future of interactive entertainment!
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