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Machine Learning Engineer Opt Jobs in Newark, NJ

ExaCare AI

Machine Learning Engineer

Manhattan, NY · On-site

$110 - $150/hr

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join our team. You will own the end‑to‑end ML lifecycle, from dataset creation and foundational ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

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 ...

Visible Hands

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $190/hr

Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning. Core Responsibilities * Architect Physics Foundation Models: Design and train deep learning ...

Canals

Machine Learning Engineer

New York, NY · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Canals

Machine Learning Engineer

New York, NY · On-site

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

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 ...

Root Access, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$140 - $190/hr

Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning. Core Responsibilities * Architect Physics Foundation Models: Design and train deep learning ...

Spgi

Machine Learning Engineer

New York, NY · On-site +1

$209K - $250K/yr

  • Medical

  • Retirement

Machine Learning Engineer Location: 55 Water Street, New York, NY 10041 (Telecommuting permitted within normal commuting distance of New York, NY office) Job Duties: Kensho Technologies LLC seeks a ...

Canals, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$120/hr

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning. Core Responsibilities * Architect Physics Foundation Models: Design and train deep learning ...

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Showing results 21-40

All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in Newark, NJ

Machine Learning Engineer Opt information

See Newark, NJ salary details

$32.9K

$134.7K

$202.3K

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

As of Aug 16, 2026, the average yearly pay for machine learning engineer opt in Newark, NJ is $134,657.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,100.00 and $162,100.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

What are popular job titles related to Machine Learning Engineer Opt jobs in Newark, NJ?

For Machine Learning Engineer Opt jobs in Newark, NJ, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Opt jobs in Newark, NJ look for?

The top searched job categories for Machine Learning Engineer Opt jobs in Newark, NJ are:

What cities near Newark, NJ are hiring for Machine Learning Engineer Opt jobs?

Cities near Newark, NJ with the most Machine Learning Engineer Opt job openings:

Machine Learning Engineer Opt jobs near you
Infographic showing various Machine Learning Engineer Opt job openings in Newark, 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 $134,657 per year, or $64.7 per hour.

Machine Learning Engineer

ExaCare AI

Manhattan, NY • On-site

$110 - $150/hr

Other

Re-posted 21 days ago

Job description

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join our team. You will own the end‑to‑end ML lifecycle, from dataset creation and foundational research to building and deploying production‑grade models. If you thrive in an environment where you can quickly iterate, experiment with cutting‑edge techniques, and see your work make a tangible impact, this is the role for you.

What You'll Do
  • Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
  • Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
  • Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
  • Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks.
  • Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
  • Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost. This includes quantization, pruning, and knowledge distillation.
  • Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high‑quality, robust training data.
  • Advanced Architectures: Stay current with and apply state‑of‑the‑art techniques, especially relating to Large Language Models (LLMs).
What You'll Bring
  • Proven experience (3+ years) in building, training, and deploying machine learning models in a production environment.
  • Expert‑level proficiency in Python.
  • Experience with modern deep learning frameworks, such as PyTorch.
  • Demonstrable experience with systematic hyperparameter searching and optimization frameworks (e.g., Optuna, Ray Tune).
  • Exceptional organizational skills, with a strong emphasis on reproducible research and methodical experiment tracking.
  • Direct experience with LLMs, including fine‑tuning, prompt engineering, RAG, and efficient inference.
  • Practical experience implementing model optimization techniques like quantization (e.g., bitsandbytes) and pruning.
  • Experience in designing and curating novel datasets from scratch.
  • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.
Bonus Points (Preferred Qualifications)
  • Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE).
  • Contributions to open‑source ML projects or a portfolio of personal projects demonstrating a passion for the field.
  • Strong, hands‑on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus).

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