Research Jobs at Kronosresearch

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Job Position Company Posted Location Salary Tags

Kronosresearch

Remote

$121k - $125k

Kronosresearch

Remote

$84k - $156k

Kronosresearch

Remote

$67k - $105k

Kronosresearch

Remote

$64k - $72k

Kronosresearch

Remote

$64k - $72k

Kronosresearch

Remote

$103k - $110k

Kronosresearch

Remote

$89k - $106k

Kronos Research

Singapore, Singapore

$64k - $72k

Kronos Research

Singapore, Singapore

$64k - $72k

Kronos Research

Singapore, Singapore

$84k - $100k

Kronos Research

Shanghai, China

$63k - $77k

Kronos Research

remote

$28k - $33k

Kronos Research

Singapore, Singapore

$122k - $135k

Kronos Research

Taipei, Taiwan

$89k - $102k

Kronos Research

Hong Kong, Hong Kong

$58k - $80k

Kronosresearch
$121k - $125k estimated
Remote

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.