Software Engineer, Machine Learning/ ML Engineer, Data Scientist
Job in
New York, New York County, New York, 10261, USA
Listed on 2026-06-29
Listing for:
Goliath-Partners
Full Time
position Listed on 2026-06-29
Job specializations:
-
Software Development
Machine Learning/ ML Engineer, Data Scientist, AI Engineer (Applied/Software)
Job Description & How to Apply Below
Our client, a quantitative trading firm that uses cutting-edge models and infrastructure to provide liquidity to the global markets, is seeking a Machine Learning Researcher. This individual will tackle complex problems without obvious solutions, taking ownership of the entire modeling ecosystem, from feature engineering and deep learning architecture design to training dynamics and execution strategy. The ideal candidate possesses a strong technical skillset, a sense of curiosity, and a collaborative mentality to drive innovations that directly influence global market operations.
Role- Investigate, evaluate, and prototype innovative algorithmic solutions using novel machine learning and deep learning techniques.
- Conduct empirical ML research across multiple problem domains, rapidly prototyping and iterating novel architectures in Python, PyTorch, or Tensor Flow.
- Partner with quantitative traders, researchers, and developers across teams to transform market insights into actionable data features and predictive models.
- Minimum of 2+ years of applied experience developing deep learning solutions across diverse fields.
- Advanced programming expertise and proficiency in rapid prototyping using Python and contemporary deep learning frameworks like PyTorch or JAX.
- A strong production, results‑oriented mindset with a focus on developing deep learning models that directly impact P&L and create bottom‑line value.
- Proven capability in applying logical and mathematical reasoning to translate and adapt cutting‑edge research methods between different problem domains.
- Experience with reinforcement learning.
- Exposure to C++ in production environments.
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