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Associate Machine Learning Jobs in New York (NOW HIRING)

Harnham

Lead Machine Learning Engineer

Manhattan, NY · On-site +1

$220K - $260K/yr

Base pay range $220,000.00/yr - $260,000.00/yr Placing the Top AI and Machine Learning Talent (NYC ... Seniority level Associate Employment type Full-time Job function Advertising, Engineering, and ...

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All JobsAssociate Machine Learning JobsAssociate Machine Learning Jobs in New York

Associate Machine Learning information

See New York salary details

$27K

$142K

$348.2K

How much do associate machine learning jobs pay per year?

As of Jul 10, 2026, the average yearly pay for associate machine learning in New York is $142,003.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $196,900.00 per year, depending on experience, location, and employer.

What is the difference between Associate Machine Learning vs Data Scientist?

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Engineer, and why are they important?

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by Associate Machine Learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What does an Associate Machine Learning Engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.
What are the most commonly searched types of Machine Learning jobs in New York? The most popular types of Machine Learning jobs in New York are:
What are popular job titles related to Associate Machine Learning jobs in New York? For Associate Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Associate Machine Learning jobs in New York look for? The top searched job categories for Associate Machine Learning jobs in New York are:
Associate Machine Learning jobs near you

Associate, Machine Learning Engineer

Cantor Fitzgerald Securities

Manhattan, NY • On-site

Full-time

Posted 23 days ago

Job description

We are seeking an early-career engineer to join our team and play a vital role in developing and enhancing AI-powered applications for our financial services business. The ideal candidate will have a solid foundation in software development, hands-on experience with modern AI tools, and a keen interest in understanding the behavior of language models in real-world applications. As an Associate, you will have the opportunity to work closely with our experienced engineers and contribute to the growth and success of our innovative AI initiatives.
Company overview: 

Built upon the foundation of innovative technology and exceptional talent, BGC is a pioneering global brokerage and financial technology company servicing the financial markets. We are agile and dynamic in our approach, delivering world-class products to our diverse customer base daily. Our Financial Services business provides a full range of trade execution and broker-dealer services. 

The benefit of BGC's integrated platform is that it gives customers flexibility and choice in price discovery, execution, and processing of their transactions, through voice, hybrid, or fully electronic brokerage options. In addition, our BGC Trader and BGC Market Data platforms offer financial technology solutions, market data and analytics related to financial instruments and markets.

Agency Notice:

BGC Group & affiliates do not accept agency resumes. Please do not forward resumes to our job alias, employees or any other company location. BGC Group & affiliates are not responsible for any fees related to unsolicited resumes. Please contact the Recruitment function for additional details. 
  • Bachelor's degree in a technical field (computer science, machine learning, mathematics, etc.) or equivalent practical experience.
  • Experience contributing to production-level software development, internships, research, or substantial personal projects.
  • Strong programming skills in Python, with a focus on writing clear, tested, and maintainable code.
  • Hands-on experience with web services, data integration, testing, logging, and monitoring.
  • Practical knowledge of building with LLMs and understanding common failure modes.
  • Ability to test, evaluate, and improve LLM-powered applications.
  • Grounding in machine learning, statistics, and experimental design, with a knack for technical documentation.
  • Excellent communication skills and a collaborative mindset.
  • Interest in applying AI responsibly in financial services.
  • Familiarity with agentic workflows, evaluation tools, and cloud deployment is a plus.

Compensation

  • Collaborate with a cross-functional team to build, evaluate, and improve AI-powered financial services applications.
  • Design and implement machine learning models and algorithms to solve complex business problems.
  • Work with large language models (LLMs) and understand their behavior and potential failure modes.
  • Conduct testing and evaluation of LLM-powered applications, analyzing failures and defining success metrics.
  • Apply machine learning, statistics, and experimental design principles to reason about model behavior.
  • Communicate effectively with product, engineering, and business partners to align on project goals.
  • Ensure responsible AI practices are followed, considering privacy, security, and appropriate automation.
  • Stay updated with the latest advancements in AI and machine learning technologies.
  • Document and present project progress and findings to stakeholders.
  • Provide support and mentorship to junior team members as needed.

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