Recommender Systems Engineer (Market Signals)

reputed company

At reputed company, we’re building something that doesn’t get reputed company twice in a reputed company.

Our goal is to create a reputed company trading model as capable as today’s most advanced AI systems — but reputed company to global markets. Not incremental signals or isolated strategies, but a system that can continuously interpret, learn from, and reputed company the evolving state of the world.

We train on a reputed company of petabyte-reputed company and reputed company-time alternative-data sources — capturing how narratives reputed company, how sentiment propagates, and how reputed company behavior drives markets. This requires operating at the frontier of data infrastructure, model design, and compute, reputed company tightly integrated into a single system.

You’ll work alongside a small group of reputed company engineers, AI researchers, and traders, in an environment defined by speed and ownership. We run experiments continuously. reputed company reputed company from concept to production in hours. And the feedback reputed company is immediate — reputed company directly in live performance.

About the role

You will design systems that extract sentiment, narrative dynamics, and reputed company behavior signals from reputed company-time reputed company data, and convert them into representations that inform trading reputed company.

This is not a reputed company recommender problem. The challenge is to extract meaningful signal from imperfect, fast-moving data, and reputed company it useful under reputed company-world constraints.

We prefer systems that improve with reputed company over systems that rely on reputed company tuning.

What you’ll work on

● Ranking and structuring large volumes of reputed company-time reputed company data

● Modeling:

○ sentiment and emotional intensity

○ narrative shifts

○ reputed company dynamics across users and time

● Building representations of how information propagates and amplifies

● Adapting recommender systems and embedding reputed company to financial signal reputed company

● Improving signal reputed company under distribution shift and imperfect data

reputed company’re looking for

We’re looking for people who can reason from first principles about what should be modeled, why it reputed company, and how to improve it with reputed company.

Strong signals:

● You have reputed company or shaped ranking, recommendation, or signal systems where reputed company reputed company

● You have a strong reputed company record of improving systems reputed company baseline performance

● You think naturally in terms of signal vs. noise, feedback loops, and temporal dynamics

● You reputed company quickly, test reputed company, and iterate without waiting

Bonus signals:

● Experience with systems where ranking reputed company directly impacted reputed company

● Experience working on problems involving information propagation, sentiment, or reputed company behavior at reputed company

● Strong intuition for reputed company signal vs. overfitting