aimlroles

Machine Learning

Cantor Fitzgerald/BGC Open · verified Oct 22, 2026
New York Full-time Mid-level Machine Learning Engineering

At a glance

Mid-level Machine Learning Engineering role at Cantor Fitzgerald/BGC. New York · full-time · $140,000–$160,000 base.

SalaryStated by Cantor Fitzgerald/BGC
$140,000 – $160,000
Base salary for this role, in USD per year, as published in the posting.
This role, from Cantor Fitzgerald/BGCOur estimate for the market: $166,050–$230,000Median $218,200
Below the market median of $218,200 for mid-level machine learning engineering roles in New York.
Worked out from 25 postings that stated pay, median $218,200. Last updated Oct 20, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationNew York
EmploymentFull-time
Base pay$140,000–$160,000
PostedSep 2, 2026 · 7w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build and launch LLM features in production, keeping performance, reliability, and responsible-AI standards in view
  • Create data pipelines for structured and unstructured information, plus reusable Python libraries with tested code
  • Develop prompts, tool-calling flows, retrieval systems, and evaluation suites to measure results and investigate failures
  • Track AI services with monitoring, logs, and alerts; address hallucinations, response delays, and operating costs
  • Work with product, engineering, and business partners, and document designs, experiments, and findings

What you bring

  • A computer science, machine learning, mathematics, physics, statistics, or econometrics degree, or equivalent practical experience
  • Practical Python strength and experience contributing to production or production-like software
  • Hands-on work with LLM features and evaluation, including test sets, metrics, failure review, and iteration
  • A grounding in machine learning, statistics, and experimental design, with experience interpreting technical papers or documentation

Who this fits

This mid-level role suits someone who has built software through a job, internship, research, open-source work, or a substantial personal project. You should be comfortable working with product, engineering, and business teams and applying AI with privacy, security, and human review in mind. The position is based in New York.

From the employer

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.
  • 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.
  • 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