Remote Jobs at Kronosresearch

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$121k - $125k

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$63k - $66k

Kronosresearch

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$84k - $156k

Kronosresearch

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$67k - $105k

Kronosresearch

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$64k - $72k

Kronosresearch

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$89k - $106k

Kronosresearch

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$36k - $60k

Kronosresearch

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$98k - $180k

Kronosresearch

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Kronosresearch

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$103k - $123k

Kronosresearch

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$68k - $90k

Kronosresearch

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$64k - $72k

Kronosresearch

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$27k - $75k

Kronosresearch

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$69k - $75k

Kronosresearch

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$59k - $62k

Kronosresearch
$121k - $125k estimated
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Role Overview We are seeking an experienced Machine Learning Researcher to join our research team. This role requires expertise in designing and deploying deep learning models within high-performance, low-latency trading systems. You will be working on developing robust, scalable models and integrating them into our trading infrastructure.   Responsibilities

Data Analysis & Preprocessing: Understand and preprocess orderbook data. Deep Learning Model Design: Design models for time-series and orderbook data (Transformers, RNNs, CNNs, Attention). Scalable Training Implementation: Implement parallelized data loading pipelines. Feature Engineering: Develop and optimize orderbook features using C++. Backtesting & Evaluation: Conduct rigorous backtesting across markets. Production Integration: Deploy models into real-time, low-latency systems.

Requirements

Background in machine learning or quantitative research, preferably related to financial markets. Experience deploying ML models in real-time, low latency environments is a plus. Familiarity with optimizing model latency and inference speed(e.g., KV caching, quantization, pruning) is advantageous. Open to both experience candidates and highly motivated fresh graduated.

Technical Skills

Deep Learning Architectures: Transformers, RNNs, CNNs, Attention mechanisms. Programming Languages: Python, C++, Jax/PyTorch Model Optimization: Optimizing models for high-performance trading systems.

Analytical & Communication Skills

Strong mathematical and statistical background (probability theory, linear algebra, calculus). Ability to articulate complex technical concepts.

Motivation & Learning

Passion for applying machine learning to quantitative finance. Drive to continuously improve models.