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# Machine Learning Engineer

- **Company:** GOOD FRIEND ELECTRICAL SUPPLIES, INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithmic Trading, Big Data, Python (Programming Language), Machine Learning, NumPy, Signal Processing, Data Streaming, Systems Integration, Deep Learning, Pandas, Information Technology, Low Latency
- **Published:** July 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p6l8rtl1id

## About the Role

* 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

## Description

* 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).


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