How much does AKUNA CAPITAL pay for data roles?

Reported total comp for data roles at AKUNA CAPITAL ranges from roughly $3k to $155k per year, varying by level, team, and location.

What roles can I prepare for at AKUNA CAPITAL?

Dataford has interview guides for 7 roles at AKUNA CAPITAL, including Data Analyst, Data Engineer, Data Scientist, and Machine Learning Engineer, and more.

Is AKUNA CAPITAL a good place to work?

Employees rate AKUNA CAPITAL 3.6 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.

Where is AKUNA CAPITAL headquartered?

AKUNA CAPITAL is headquartered in Chicago, IL.

Interview Guides

AKUNA CAPITALMachine Learning Engineer

Updated Jul 5, 2026

AKUNA CAPITAL Machine Learning Engineer interview questions & guide 2026

Every question AKUNA CAPITAL interviewers actually ask, the frameworks that win the room, and the language hiring managers respond to.

Question bank

What is a Machine Learning Engineer at AKUNA CAPITAL?

As a Machine Learning Engineer at AKUNA CAPITAL, you are stepping into a highly competitive, data-driven proprietary trading environment where your models directly impact the firm’s trading strategies and profitability. AKUNA CAPITAL specializes in derivatives market making and quantitative trading across various asset classes, including options and cryptocurrencies. In this role, you bridge the gap between complex quantitative research and robust, low-latency production engineering.

Your impact will be felt across multiple dimensions of the business. You will be responsible for designing, building, and optimizing the machine learning pipelines that process terabytes of financial data in real-time. Because the role often operates at the intersection of modeling and infrastructure—frequently functioning as a hybrid Machine Learning Data Engineer—your work ensures that quantitative researchers and traders have access to the cleanest data and the most performant predictive models available.

Expect to work on challenging problems involving massive scale, strict latency constraints, and highly volatile markets. You will collaborate closely with quants, software engineers, and traders to deploy models that predict market movements, optimize pricing algorithms, and manage risk. This role is critical; at AKUNA CAPITAL, superior technology and sharper models are the primary drivers of competitive advantage.

Common Interview Questions

The questions below represent patterns frequently encountered by candidates interviewing for the Machine Learning Engineer and Data Engineer roles at AKUNA CAPITAL. While you should not memorize answers, use these to understand the depth and style of the technical evaluation.

Algorithms and Data Structures

This category tests your core programming fundamentals and your ability to write optimal code for complex problems.
Write an algorithm to find the Kth largest element in a stream of incoming data.
Implement a thread-safe queue in Python or C++.

Given a list of trading intervals, merge all overlapping intervals.
Design a system to efficiently find the median of a massive, continuously updating array of integers.
Write a function to perform a deep copy of a graph structure.

Machine Learning and Statistics

These questions evaluate your theoretical understanding of ML algorithms and your ability to apply statistical concepts to data.
Explain the bias-variance tradeoff and how it impacts model complexity.
How do you detect and handle data leakage when building a predictive model?
Walk me through the mathematics of gradient descent.
What is the difference between generative and discriminative models?
Given a dataset with missing values, describe three different imputation strategies and their trade-offs.

Data Engineering and Systems

This area focuses on your ability to build the infrastructure that supports data-intensive ML applications.
How would you design a distributed system to process and store high-frequency order book data?
Explain the differences between row-oriented and column-oriented databases. When would you use each?
Describe a time you had to optimize a slow data pipeline. What steps did you take?
How do you ensure data quality and integrity in a real-time streaming architecture?
Design a schema for storing historical options pricing data.

Probability and Brainteasers

These questions test your on-the-spot mathematical reasoning and logical deduction.
You have two ropes, each taking exactly one hour to burn, but they burn at uneven rates. How do you measure exactly 45 minutes?
What is the probability that three randomly selected points on a circle fall within the same semicircle?
You are playing a game where you flip a coin until you get tails. You win $2^n$ where n is the number of heads. How much would you pay to play this game?
Explain the Monty Hall problem and prove the optimal strategy mathematically.

Deep Dive into Evaluation Areas

To succeed, you must excel across several distinct technical and quantitative domains. AKUNA CAPITAL interviewers will rigorously probe your limits in the following areas.

Coding and Algorithms

This area tests your ability to write efficient, production-ready code under time constraints. In a low-latency trading environment, poorly optimized code translates directly to lost revenue. Interviewers expect you to quickly identify the optimal data structures and algorithms for a given problem.
Strong performance means writing bug-free code on the first pass, clearly explaining your space and time complexity, and proactively identifying edge cases.

Be ready to go over:

Data Structures – Arrays, hash maps, heaps, trees, and graphs.
Algorithmic Paradigms – Dynamic programming, sliding window techniques, and graph traversal (BFS/DFS).
Optimization – Memory management, multithreading, and concurrency in Python or C++.
Advanced concepts (less common) – Bit manipulation, custom memory allocators, and lock-free data structures.

Example questions or scenarios:

"Implement a sliding window algorithm to calculate the moving average of a real-time data stream."
"Design an efficient cache with a specific eviction policy (e.g., LRU or LFU) using fundamental data structures."
"Write a function to detect cycles in a directed graph representing trading dependencies."

Machine Learning Theory and Applied Modeling

This section evaluates your understanding of the math behind the models and your practical ability to apply them. You are not just using APIs; you need to understand how algorithms behave under the hood, especially with noisy financial data.
Strong candidates can explain the trade-offs between different models, discuss how to handle overfitting, and articulate the statistical assumptions underlying their choices.

Be ready to go over:

Supervised/Unsupervised Learning – Linear/logistic regression, decision trees, random forests, and clustering algorithms.
Deep Learning – Neural network architectures, backpropagation, and optimization algorithms (Adam, SGD).
Model Evaluation – Cross-validation, precision/recall, ROC-AUC, and handling imbalanced datasets.
Advanced concepts (less common) – Time-series forecasting (ARIMA, LSTMs), reinforcement learning, and natural language processing for sentiment analysis.

Example questions or scenarios:

"Explain the mathematical difference between L1 and L2 regularization, and when you would use each."
"How would you handle a dataset with severely imbalanced classes when predicting rare market events?"
"Walk me through the architecture of a recurrent neural network designed for time-series prediction."

Data Engineering and Systems

Given the hybrid nature of the Machine Learning Data Engineer role, you must prove you can build the infrastructure that feeds your models. AKUNA CAPITAL deals with massive, high-velocity data streams.
Interviewers will look for your ability to design scalable, fault-tolerant data pipelines. Strong performance involves a clear understanding of distributed systems, database indexing, and batch versus streaming data processing.

Be ready to go over:

Distributed Systems – Concepts of partition tolerance, replication, and consensus.
Data Processing Frameworks – Experience with Apache Spark, Kafka, or similar big data tools.
Database Management – SQL optimization, schema design, and NoSQL databases.
Advanced concepts (less common) – Low-latency network protocols, hardware-software co-design, and cloud infrastructure optimization.

Example questions or scenarios:

"Design a data pipeline that ingests high-frequency tick data, aggregates it, and serves it to a machine learning model in real-time."
"How would you optimize a slow SQL query that is joining two massive tables of historical trade data?"
"Explain the differences between processing data in batch versus processing data in a stream."

Probability, Statistics, and Brainteasers

Proprietary trading firms are deeply quantitative. You will be tested on your mathematical intuition and your ability to calculate probabilities on the fly.
Strong performance requires staying calm, thinking aloud, and applying foundational probability rules (like Bayes' Theorem) to solve abstract puzzles or dice games.

Be ready to go over:

Combinatorics and Probability – Expected value, variance, permutations, and combinations.
Statistical Distributions – Normal, binomial, Poisson, and uniform distributions.
Hypothesis Testing – p-values, A/B testing frameworks, and confidence intervals.
Advanced concepts (less common) – Stochastic calculus, Markov chains, and Monte Carlo simulations.

Example questions or scenarios:

"What is the expected number of coin flips needed to get two consecutive heads?"
"If you roll a fair six-sided die, what is the expected value of the roll? What if you are allowed to reroll once?"
"Explain Bayes' Theorem and apply it to a scenario where a trading signal has a known false positive rate."

AKUNA CAPITAL Machine Learning Engineer Interview Questions 2026 | Dataford

AKUNA CAPITAL Machine Learning Engineer interview questions & guide 2026