Balyasny Asset Management

Large Enterprises

Balyasny Asset Management is a global multi-strategy hedge fund founded in 2001 and headquartered in Chicago. It manages tens of billions of dollars across equities, macro, commodities, and other strategies, employing a multi-manager investment model.

4 interview notes · updated Jul 2026

Balyasny Asset Management·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at Balyasny Asset Management, which is a quant hedge fund so the questions were pretty squarely focused on applying deep learning to messy financial data rather than anything generic.

Financial data has an extremely high noise-to-signal ratio. How would you modify a deep learning model to handle that?

“This one actually made me pause.”

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Balyasny Asset Management·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at Balyasny Asset Management. Just the one technical question that I can remember, so either it was a short screen or I've blocked the rest out.

How would you approach anomaly detection?

“Broad question and I kind of rambled.”

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Balyasny Asset Management·Machine Learning Engineer·Technical Phone Screen

May 2026

Interviewed for an ML Engineer role at Balyasny Asset Management. One question stuck with me more than the others, a pretty open-ended problem about dealing with noisy labels in financial data.

Financial data often comes with noisy or unreliable labels. How would you go about constructing labels in that kind of environment?

“This one exposed a gap I didn't know I had.”

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Balyasny Asset Management·AI Engineer·Technical Phone Screen

Apr 2026

Live coding round for an AI Engineer role at Balyasny Asset Management. Online search was allowed, which sounds like a relief until you realize they still drill pretty hard on the conceptual stuff underneath the code.

Implement logistic regression from scratch, including any feature engineering you think is appropriate.
How would you handle categorical features here? Walk through the tradeoffs between one-hot encoding and target encoding.
What do the coefficients in logistic regression actually mean? How do you interpret them?
When would you choose a gradient boosted tree model or a neural network over logistic regression?

“The open-search policy made me overconfident at first.”

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