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This listing was closed on Sep 18, 2026.
Browse Open JobsMachine Learning Engineer
Summary by AIEngineer.careers
Machine Learning Engineer is a Machine Learning Engineer role (full-time). with Voleon. in REMOTE, NEW YORK, US. Compensation shown: $290K–$395K. Imported listing (source: remotefront.com). Apply on the employer's site (remotefront.com).
Imported listing (source: remotefront.com) · Apply on remotefront.com
REMOTE · NEW YORK · US
$290K–$395K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from remotefront.com
Voleon applies state-of-the-art AI and machine learning to quantitative trading. As a Senior Machine Learning Engineer, you'll partner with PhD researchers to design, implement, and productize ML models, and build the data pipelines and infrastructure underpinning trading strategies. This role combines strong software engineering with mathematical rigor.
Responsibilities
- Partner with PhD researchers to design, implement, and productize ML models for quantitative trading.
- Develop and maintain complex data pipelines: ingestion, feature engineering, validation, quality monitoring.
- Translate research prototypes into performant, well-tested production code.
- Build tools and frameworks that accelerate model development and experimentation.
- Supervise and remediate subtle data quality issues across research and production.
- Lead projects from requirements to delivery and align with research and engineering stakeholders.
- Guide junior engineers and enforce engineering best practices.
Requirements
- Bachelor's degree (or higher) in CS, Applied Math, Statistics, or related quantitative field.
- 5+ years of professional software engineering experience with strong CS fundamentals.
Voleon
Berkeley · US · 430+ employees
The Voleon Group is a quantitative investment management firm that utilizes artificial intelligence and machine learning in its trading strategies. It focuses on solving large-scale financial prediction problems through rigorous data-driven techniques and statistical analysis.
