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Cantor Fitzgerald

Machine Learning Engineer

Cantor Fitzgerald
United States · Full-time · Associate

Qualifications

  • Bachelor's degree in a technical subject (e.g. computer science, machine learning, mathematics, physics, statistics, econometrics), or equivalent experience
  • Ability to write code fluently in at least one programming language, preferably Python
  • Knowledge of mathematics, statistics and machine learning concepts needed to properly train and evaluate production models and understand research papers
  • Ability to communicate technical ideas effectively and work with business partners
  • Entrepreneurial mindset and strong interest in using LLMs and classical machine learning techniques to help drive a multi-billion dollar business
  • 2+ years experience in a machine learning, data engineering or software engineering team


Nice To Have


  • Familiarity with deploying and managing containerized applications in an AWS and/or Azure environment
  • Familiarity with Infrastructure as Code (Terraform) and CI/CD
  • Experience building RESTful APIs (preferably in Flask, FastAPI, or other Python frameworks)
  • Experience building data pipelines to feed ML models
  • Knowledge of Python ML/data/math libraries such as PyTorch, numpy and pandas


The expected base salary for this position ranges from $ 175,000 to $ 250,000. The actual base salary will be determined on an individualized basis taking into account a wide range of factors including, but not limited to, relevant skills, experience, education, and, where applicable, licenses or certifications held. In addition to base salary and a competitive benefits package, this position may be eligible for additional types of compensation including discretionary bonuses and other short- and long-term incentives (e.g., deferred cash, equity, etc.).

Key Skills

Ranked by relevance

machine learning python infrastructure as code restful apis terraform fastapi pytorch flask numpy aws
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Posted
Mar 10, 2025
Type
Full-time
Level
Associate
Location
New York

Industries

Financial Services

Categories

Engineering Information Technology

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