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Graduate Machine Learning Engineer Jobs in New York

Machine Learning Engineer

New York, NY ยท Hybrid

$90K - $254K/yr

We are in search of an exceptional Machine Learning Engineer to join our accomplished team. In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary ...

Lead Machine Learning Engineer

New York, NY ยท On-site

$112K - $147K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

Lead Machine Learning Engineer

New York, NY ยท On-site +1

$112K - $147K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Lead Machine Learning Engineer

Manhattan, NY ยท On-site +1

$112K - $148K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform. Machine ...

We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform. Machine ...

Machine Learning Engineer

New York, NY ยท On-site +1

$223K - $260K/yr

Collaborate closely with multiple stakeholders across product, engineering, research and marketing. Train, evaluate, test, and deploy machine learning models. Part-time telecommuting is an option.

Machine Learning Engineer

New York, NY ยท On-site

$85K - $125K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

About the Role We are looking for a Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This is an engineering-focused role centered on building the ...

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our ...

Machine Learning Engineer

New York, NY ยท On-site

$200K - $300K/yr

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our ...

Sr. Lead Machine Learning Engineer

New York, NY ยท On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

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Graduate Machine Learning Engineer information

See New York salary details

$34.5K

$140.9K

$211.7K

How much do graduate machine learning engineer jobs pay per year?

As of Jul 3, 2026, the average yearly pay for graduate machine learning engineer in New York is $140,878.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $169,600.00 per year, depending on experience, location, and employer.

What does a Graduate Machine Learning Engineer do?

A Graduate Machine Learning Engineer is an entry-level professional who designs, develops, and tests machine learning models and algorithms. They work with data scientists and engineers to preprocess data, train models, and deploy solutions to solve real-world problems. Their responsibilities often include coding in languages like Python, using libraries such as TensorFlow or PyTorch, and staying updated with the latest advancements in machine learning. This role serves as a starting point for a career in AI, providing hands-on experience in building and optimizing intelligent systems.

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

To thrive as a Graduate Machine Learning Engineer, you need a solid foundation in computer science, mathematics (especially statistics and linear algebra), and proficiency in programming languages like Python, often supported by a relevant degree. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), version control systems (like Git), and experience with cloud platforms or data management tools are typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you collaborate and translate complex concepts into practical solutions. These skills and qualities are crucial for developing robust models, integrating them into real-world applications, and contributing effectively to multidisciplinary teams.

What are some common challenges faced by Graduate Machine Learning Engineers during their first year, and how can they overcome them?

Graduate Machine Learning Engineers often encounter challenges such as bridging the gap between academic knowledge and real-world application, working with large or messy datasets, and learning to collaborate within cross-functional teams. Adapting to production-level code standards and understanding existing codebases can also be demanding. To overcome these hurdles, it's helpful to seek mentorship from experienced colleagues, actively participate in code reviews, and invest time in learning best practices for data preprocessing and model deployment. Embracing continuous learning and open communication will ease the transition into the professional environment.

What is the difference between Graduate Machine Learning Engineer vs Data Scientist?

AspectGraduate Machine Learning EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related field; some internshipsBachelor's or Master's in Statistics, Data Science, or related field; often with experience
Work EnvironmentDeveloping ML models, coding, testing algorithmsAnalyzing data, creating visualizations, deriving insights
Employer & Industry UsageTech companies, startups, research labsFinance, healthcare, tech, consulting firms

While both roles involve working with data and algorithms, Graduate Machine Learning Engineers focus on developing and deploying machine learning models, often requiring coding and technical skills. Data Scientists analyze data to extract insights and inform decisions. The roles overlap in skills but differ in primary responsibilities and focus areas.

Infographic showing various Graduate Machine Learning Engineer job openings in New York as of June 2026, with employment types broken down into 1% Internship, 59% Full Time, 38% Part Time, 1% Temporary, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $140,878 per year, or $67.7 per hour.
Machine Learning Engineer

Machine Learning Engineer

WITHIN

New York, NY โ€ข Hybrid

$90K - $254K/yr

Other

Medical, Dental, Vision, Retirement, PTO

This job post hasย expired 1 day ago.ย Applications are no longer accepted.


Job description

About the Role: We are in search of an exceptional Machine Learning Engineer to join our accomplished team. In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary focus on Ad Score and Ad Account Health. You will play a crucial part in delivering actionable insights and solutions to our clients, and your work will be integral to our mission.

Responsibilities include but are not limited to;

  • ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health.
  • Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization.
  • Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training.
  • Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement.
  • Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs.
  • Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products.
  • Research and Innovation: Stay up-to-date with the latest developments in the field of data science and machine learning, and explore innovative approaches to problem-solving.

Requirements

  • Master's or Ph.D. in a related field with a strong academic background.
  • Proven experience as a Data Scientist with a track record of developing and deploying predictive ML models.
  • Expertise in machine learning techniques, including but not limited to regression, classification, clustering, and deep learning.
  • Proficiency in data manipulation, feature engineering, and model evaluation.
  • Strong programming skills in languages such as Python and experience with libraries like TensorFlow, PyTorch, or scikit-learn.
  • Excellent communication skills and the ability to collaborate effectively within cross-functional teams.
  • A passion for continuous learning and staying updated with the latest trends and technologies in data science.
  • Strong problem-solving abilities and the capacity to translate complex data into actionable insights.
Required knowledge of:
  • Python
  • SQL
  • Cloud Platforms (GCP, AWS, Azure)
  • Data Warehouses (BigQuery, Snowflake, Redshift)
  • LLMs / AI APIs
  • Git / GitHub
Nice to have:
  • Data Transformation (dbt)
  • Semantic Layers (Cube, Looker, dbt Metrics)
  • TypeScript
  • Bayesian modeling experience - ideally Marketing Mix Models (PyMC, Stan, or similar..). Understands priors, MCMC sampling, posterior diagnostics.
  • Causal inference / experimentation- geo experiments (matched markets), A/B testing at scale. Familiar with incrementality measurement.
  • Marketing/advertising domain- understanding of attribution, media channels (paid social, search, display, video), campaign structures.
  • Nice to have - familiarity with adstock/saturation curves and budget optimization

Our interview process includes, but is not limited to the following:

  • Excel and Typing Test

We offer a competitive salary and benefits based on ability level, including:

  • Unlimited vacation policy
  • Monthly Phone Stipend
  • Comprehensive Medical, Dental, and Vision insurance options
  • 401(K) plan with matching
  • Dog friendly office
  • Hybrid work opportunity
  • Professional Development Program
  • Bonus Perk - Seamless allowance

Total compensation based on education, experience, and skills level ($90,900-$254,100)

  • Level 1 - Possesses essential capabilities

    • $90,900-$123,540

  • Level 2 - Possesses developing capabilities

    • $123,540-$156,180

  • Level 3 - Possesses notable capabilities.

    • $156,180-$188,820

  • Level 4 - Possesses strong capabilities.

    • $188,820-$221,460

  • Level 5 - Possesses advanced capabilities.

    • $221,460-$254,100