Machine Learning Engineer

Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AI‑driven solutions for a high‑volume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsible‑AI standards.

• Bachelor’s degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
• Experience contributing to production or production‑like software through work, internships, research, open source, or substantial personal projects.
• Strong programming ability in Python with clear, tested, and maintainable code.
• Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
• Hands‑on experience building with LLM tools or frameworks (prompting, structured outputs, tool‑calling, retrieval, multi‑step workflows) and awareness of common failure modes.
• Experience evaluating LLM‑powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
• Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
• Strong communication skills and comfort working with product, engineering, and business partners.
• Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
• Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000

• Design and implement LLM‑driven features in production systems.
• Build and maintain data pipelines for both structured and unstructured data.
• Write clean, testable Python code and maintain reusable libraries.
• Develop prompts, tool‑calling workflows, and retrieval pipelines.
• Create evaluation suites, define success metrics, and analyze failures.
• Diagnose and mitigate hallucination, latency, and cost issues.
• Collaborate with product, engineering, and business stakeholders.
• Implement monitoring, logging, and alerting for AI services.
• Contribute to responsible‑AI guardrails and human‑in‑the‑loop processes.
• Document designs, experiments, and findings for internal knowledge sharing.