Machine Learning Engineer

An elite, boutique Quantitative Trading & Algo-Trading enterprise specializing in high-performance crypto-asset management and automated investment strategies.

The venture was established by highly prominent, industry-recognized pioneers with an outstanding track record in executive positions at the world's most successful algorithmic trading firms.

Backed by extensive, multi-year institutional capital reserves ensuring long-term financial stability, the firm is currently expanding its core foundational engineering and research team.

The R&D laboratory is based in a highly accessible location within the Sharon Region / Central District (directly adjacent to the main rail transit network), operating under a hybrid model with 1 day of remote work per week. The company offers a highly streamlined, rapid, and focused interview process.

Position Overview-

• Founding Core AI & Quantitative Data Scientist (End-to-End) serving as a critical, high-impact technical pillar of the company's research and production engines, reporting directly to the Founder.

• Driving full-lifecycle algorithmic development: leading deep academic and market research, formulating Proof of Concepts (PoC), training advanced predictive models, and deploying live production-grade trading strategies.

• Designing and optimizing high-throughput statistical architectures to process, analyze, and extract features from massive, high-velocity financial datasets and alternative data streams.

• Fine-tuning, backtesting, and validating complex models in live, volatile market environments to ensure absolute execution precision and low-latency response times.

• Core Domain & Ecosystem- Algorithmic Trading & Quantitative Finance, Time-Series Forecasting, Signal Processing, High-Velocity Big Data, Predictive Machine Learning, Deep Learning Architectures, Python Core, Rust Engineering, Financial Data Frameworks (Pandas, NumPy).

Requirements-

• Academic Background: B.Sc. in a quantitative, scientific, or highly mathematical discipline (e.g., Computer Science, Data Science, Mathematics, Physics, Statistics, or Electrical Engineering) – Mandatory

• 5 years of proven professional experience dedicated to Machine Learning, Deep Learning, or Data Science within product-driven environments – Mandatory

• Recent, hands-on professional background specializing in Time-Series modeling, predictive sequence analysis, or signal processing – Mandatory

• Production-grade coding proficiency and architectural comfort in Python (specifically utilizing Pandas and NumPy) or Rust – Mandatory

• Prior experience handling high-volume, noisy, or unstructured time-series data streams – Mandatory

• Practical experience or strong technical affinity for quantitative environments, financial datasets, or low-latency system integration – Strong Advantage

• A highly autonomous, product-driven engineer with an entrepreneurial spirit, capable of moving seamlessly between abstract mathematical research and rigid production coding – Mandatory