Machine Learning Engineer

Cantor Fitzgerald; New York (NY); On-site; USD 90,000 - 130,000; Full time

Job summary

Cantor Fitzgerald L.P. is seeking an early-career engineer to help build, evaluate, and improve AI-powered applications for a large-scale financial services business.

Candidates should have strong software fundamentals, hands-on experience with modern AI tools, and curiosity about how language-model systems behave in real products. You will collaborate with product, engineering, and business partners to deliver reliable AI solutions that meet privacy and security requirements.

Qualifications

• Bachelor's degree in a technical field or equivalent practical experience.
• Experience contributing to production or production-like software.
• Strong programming ability in Python with clear, tested, maintainable code.
• Experience with web services, data integrations, testing, logging, and basic monitoring.
• Hands-on experience with LLM tools or frameworks and prompting, tool-calling, retrieval, or multi-step workflows.
• Exposure to testing or evaluating LLM-powered applications and improving prompts or retrieval.

Responsibilities

• Build, evaluate, and improve AI-powered applications for a large-scale financial services business.
• Collaborate with product, engineering, and business partners to deliver reliable AI solutions.
• Address failure modes such as hallucination, grounding issues, and latency.

Skills

Python

LLM frameworks

Web services / data integrations

Testing / evaluation of AI apps

Communication skills

ML fundamentals

Education

Bachelor’s Degree

Tools

LLM tools/frameworks

Testing frameworks

Job description

Cantor Fitzgerald L.P., with over 16,000 employees, has been a leading global financial services firm at the forefront of financial and technological innovation since 1945. Cantor Fitzgerald & Co. is a preeminent investment bank serving more than 5,000 institutional clients around the world, recognized for its strengths in fixed income and equity capital markets, investment banking, SPAC underwriting, PIPE placements, commercial real estate, and for its global distribution platform. Capitalizing on the firm’s financial acumen and technology prowess, Cantor’s portfolio of businesses also includes Prime Brokerage, Asset Management, and other businesses and ventures. For 79 years, Cantor has consistently fueled the growth of original ideas, pioneered new markets, and provided superior service to clients. Cantor operates trading desks in every major financial center globally, with offices in over 30 locations around the world.

As one of the few remaining private partnerships on Wall Street, Cantor has the distinct ability to focus on long-term value creation and solid relationship building. Our structure allows us to respond quickly to client needs, develop solutions that address complex challenges, avoid the limitations of bureaucracy, and attract talented individuals who are driven to succeed.

We're looking for an early-career engineer to help build, evaluate, and improve AI-powered applications for a large-scale financial services business. It's best suited to someone with strong software fundamentals, hands-on experience with modern AI tools, and curiosity about how language-model systems behave in real products.

Required Qualifications

• Bachelor's degree in a technical field (computer science, machine learning, mathematics, physics, statistics, econometrics) or equivalent practical experience.
• Experience contributing to production or production-like software, whether through work, internships, research, open source, or substantial personal projects.
• Strong programming ability in at least one language, preferably Python, with clear, tested, maintainable code.
• Experience working with web services, data integrations, testing, logging, and basic monitoring, across both structured and unstructured data.
• Hands‑on experience building with large language model (LLM) tools or frameworks — some mix of prompting, structured outputs, tool‑calling, retrieval, or multi‑step workflows — and awareness of common failure modes like hallucination, poor grounding, prompt sensitivity, cost, and latency.
• Exposure to testing or evaluating LLM‑powered applications: building test sets, reviewing failures, defining success metrics, and improving prompts or retrieval based on what you observe.
• Practical grounding in machine learning, statistics, and experimental design, with the ability to reason about model behavior and learn from technical papers and documentation.
• Strong communication skills, comfort working with product, engineering, and business partners, and interest in applying AI responsibly in financial services (privacy, security, human review, appropriate use of automation).

Nice to Have

• Familiarity with common agentic workflows and orchestration frameworks and with standards for connecting models to tools and data.
• Familiarity with common evaluation and observability tools.
• Exposure to human‑in‑the‑loop workflows, guardrails, or responsible‑AI practices for higher‑stakes applications.
• Familiarity with cloud deployment, containers, and modern release pipelines.
• Awareness of fine‑tuning methods and when they're worth using.

Educational Qualifications:

• Bachelor’s Degree required

The actual base salary will be determined on an individualized basis considering 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 (including health, vision, and dental insurance, paid time off and a 401(k) retirement), 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.).