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Machine Learning Algorithms Jobs in Princeton, NJ

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

Ground breaking and developing innovative machine learning algorithms and models that propel our AI products. * Build production models for anomaly detection, predictive maintenance and usage ...

Machine Learning Compiler

New York, NY · On-site

$140K - $211K/yr

As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques ... learning algorithms, models, or frameworks in alignment with product roadmap. Level of ...

Research and implement appropriate ML algorithms and tools * Develop machine learning applications according to requirements * Select appropriate datasets and data representation methods * Run ...

This person will implement and develop machine learning models to enhance our platform ... Algorithm Optimization: Continuously test and refine algorithms to improve accuracy and efficiency.

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Machine Learning Algorithms information

See Princeton, NJ salary details

$26.7K

$44.6K

$92.2K

How much do machine learning algorithms jobs pay per year?

As of Aug 14, 2026, the average yearly pay for machine learning algorithms in Princeton, NJ is $44,639.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,100.00 and $48,200.00 per year, depending on experience, location, and employer.

What are machine learning algorithms?

Machine learning algorithms are computational methods that enable computers to learn patterns and make decisions or predictions from data without being explicitly programmed for each task. These algorithms can be classified into categories such as supervised learning, unsupervised learning, and reinforcement learning, each suited for different data and goals. Examples include decision trees, support vector machines, neural networks, and clustering algorithms. The choice of algorithm depends on the type of problem, the nature of the data, and the desired outcome.

What are the key skills and qualifications needed to thrive as a machine learning algorithms engineer?

To excel as a Machine Learning Algorithms Engineer, you need a solid background in mathematics, statistics, programming (especially Python or R), and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow, PyTorch, or scikit-learn), data preprocessing tools, and cloud platforms is typically required, along with knowledge of version control systems. Strong analytical thinking, problem-solving abilities, and effective communication skills set top performers apart in this role. These skills and qualities are critical for designing robust models, collaborating with cross-functional teams, and translating complex data into actionable solutions.

What is the difference between Machine Learning Algorithms vs Data Scientists?

AspectMachine Learning AlgorithmsData Scientists
CredentialsKnowledge of algorithms, programming, statisticsAdvanced degrees in data science, statistics, or related fields
Work EnvironmentDeveloping, testing, and tuning algorithmsAnalyzing data, building models, interpreting results
Industry UsageEmbedded within data science workflows and toolsLeading data analysis projects, decision-making

While machine learning algorithms are the core tools used by data scientists, the role of a data scientist encompasses understanding, applying, and interpreting these algorithms within broader data analysis and business contexts. Machine learning algorithms are technical components, whereas data scientists integrate these tools to derive insights and inform strategies.

What are some common challenges faced when collaborating with cross-functional teams as a machine learning algorithms specialist?

As a Machine Learning Algorithms specialist, collaborating with cross-functional teams such as data engineers, software developers, and product managers can present challenges like aligning on project goals, communicating complex technical concepts to non-experts, and integrating models into existing systems. It's important to establish clear communication channels, define shared objectives early, and actively participate in iterative feedback cycles. These practices help ensure that machine learning solutions are both technically sound and aligned with business needs.

What are popular job titles related to Machine Learning Algorithms jobs in Princeton, NJ?

For Machine Learning Algorithms jobs in Princeton, NJ, the most frequently searched job titles are:

What job categories do people searching Machine Learning Algorithms jobs in Princeton, NJ look for?

The top searched job categories for Machine Learning Algorithms jobs in Princeton, NJ are:

Infographic showing various Machine Learning Algorithms job openings in Princeton, NJ as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $44,639 per year, or $21.5 per hour.

Machine Learning Engineer / AI Engineer

1 point system

New York, NY • Remote

Contractor

Posted 17 days ago


Job description

Must Haves:
Neural networks
NLP
Python
AZURE
Pytorch or tensorflow
Job Description:
Machine Learning Engineer / AI Engineer Role

Role Overview

This role is focused on developing, deploying, and optimizing machine learning models for enterprise applications. The ideal candidate should have strong hands-on experience with machine learning algorithms, neural networks, NLP, Python/R/SQL, modern ML frameworks, Microsoft Azure, and DevOps/MLOps practices. This is not just a data science research role — the candidate needs to be able to build models and support deployment/management in a production environment.


Must-Have Skills

The candidate must have hands-on experience with:

  • Supervised and/or unsupervised machine learning algorithms
  • Neural networks
  • Natural Language Processing, NLP
  • Python
  • R
  • SQL
  • TensorFlow, Keras, and/or PyTorch
  • Microsoft Azure cloud platform
  • DevOps and/or MLOps practices
  • Model development, deployment, optimization, and lifecycle management