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Associate Machine Learning Jobs in New York (NOW HIRING)

Jobtailor

Associate Director, Data Science

Manhattan, NY · On-site

$130 - $180/hr

• Partner with Planning & Investment teams to provide hands‑on Data Science support across the entire campaign lifecycle • Build advanced machine learning models to cluster and segment ...

New York Life

Associate - Data Scientist

Manhattan, NY · On-site

$81K - $115K/yr

Execute AI and machine learning initiatives in partnership with data, technology, product, and business teams. Contribute to the end-to-end model lifecycle, including data exploration, feature ...

New York University

POSTDOCTORAL ASSOCIATE

New York, NY · On-site

$65K/yr

Description POSTDOCTORAL ASSOCIATE New York University Tandon School of Engineering The Department ... The successful candidate will have a demonstrated background in machine learning, physical ...

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All JobsAssociate Machine Learning JobsAssociate Machine Learning Jobs in New York

Associate Machine Learning information

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$142K

$348.2K

How much do associate machine learning jobs pay per year?

As of Aug 21, 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 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 key skills and qualifications needed to thrive as an associate machine learning engineer?

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 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 most commonly searched types of Machine Learning jobs in New York?

The most popular types of Machine Learning jobs in New York are:

What cities in New York are hiring for Associate Machine Learning jobs?

Cities in New York with the most Associate Machine Learning job openings:

Associate Machine Learning jobs near you

Applied AI/ML Senior Associate

JPMorgan Chase & Co

Manhattan, NY

$65K - $65K/yr

Full-time

Medical, Retirement

Re-posted 11 days ago

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 495 frontline employees who took The Breakroom Quiz

71st of 171 rated banks

Job description

As part of the Commercial & Investment Bank, J.P. Morgan Payments enables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money and assets. We tackle complex challenges at every stage of the payment lifecycle and our industry-leading solutions facilitate seamless transactions across borders, industries and platforms.  Operating in over 160 countries and handling more than 120 currencies, we are the largest processor of USD payments, with a daily transaction volume of $10 trillion.

As a Senior Associate Applied AI/ML Scientist within our payment solutions team, you will be instrumental in utilizing artificial intelligence and machine learning technologies to augment our services and stimulate business expansion. Your role will involve researching, experimenting, developing, and implementing high-quality machine learning models, services, and platforms to streamline payment processes, bolster fraud detection, and enrich customer experience. You will also be tasked with designing and executing highly scalable and dependable data processing pipelines, conducting analysis, and deriving insights to boost and optimize business outcomes. Collaborating with cross-functional teams to pinpoint opportunities for AI/ML applications within the payment's ecosystem will also be a part of your responsibilities. 

Job Responsibilities: 

  • Actively collaborate with Product, Technology, and other cross-functional teams to gain a deep understanding of complex business problems and formulate data-driven solutions to address these challenges in key areas of the payments' domain. 
  • Design, develop, and deploy agentic systems using Large Language Models (LLM), machine learning and other AI solutions that meet success metrics aligned with business goals, while considering constraints such as model complexity, scalability, and latency. 
  • Partner with Risk and Compliance teams to ensure comprehensive model documentation, track performance metrics, and maintain adherence to regulatory compliance standards. 
  • Translate model outcomes into business impact metrics and communicate complex concepts to senior management and stakeholders. 

Required qualifications, capabilities, and skills: 

  • Master's degree in a quantitative discipline (e.g., Computer Science, Data Science, Mathematics/Statistics, or Operations Research) with a minimum of 3 years of industry experience. Experience with Shell Scripting, Jupyter notebook/Lab, SQL, PySpark, and AWS Cloud Services is required. 
  • Proficient in Python with hands-on experience in Machine learning and Deep learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Experience with Jupyter Notebook/Lab is essential. 
  • Extensive knowledge in the design and development of agentic systems and the tooling ecosystem such as LangChain, LangGraph, Model Context Protocol (MCP), DSPy, etc.   
  • Solid Understanding of algorithms in machine learning, AI, and neural network, including Large Language Models (LLM) and Generative AI as well as familiarity with state-of-the-art practices and advancements in these domains. 
  • Ability to set the analytical direction for projects, transforming vague business questions into structured analytical plans. You possess strong cognitive and communication skills, characterized by clear and articulate expression. You excel at identifying core issues, bringing order to chaos, synthesizing insights, and driving decisive outcomes. 
  • Extensive experience in Natural Language Processing (NLP) or Large Language Models (LLM),or Computer Vision and other machine learning techniques, including classification, regression algorithms. 

Preferred Qualifications, capabilities and skills 

  • Experience in the financial services industry, particularly within investment banking operations. 
  • Cloud computing: Amazon Web Service, Azure, Docker, Kubernetes, DataBricks, Snowflakes. 
  • Familiarity with inference-time algorithms such as Chain of Thought and sampling, etc. 
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans

J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

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