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Machine Learning Engineer Opt Jobs in New Jersey

MLOps Engineer / DevOps Engineer

Mahwah, NJ ยท On-site

$53 - $72.50/hr

From machine learning and computer vision to Generative AI applications, our success depends on ... 1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Lead, Machine Learning Engineer

Newark, NJ ยท On-site

$107K - $141K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Lead, Machine Learning Engineer

Newark, NJ

$107K - $141K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

AI / Machine Learning Engineer

Woodbridge, NJ ยท On-site

$100 - $130/hr

  • PTO

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

Showing results 21-40

Machine Learning Engineer Opt information

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 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 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 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 popular job titles related to Machine Learning Engineer Opt jobs in New Jersey?

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

What cities in New Jersey are hiring for Machine Learning Engineer Opt jobs?

Cities in New Jersey with the most Machine Learning Engineer Opt job openings:

MLOps Engineer / DevOps Engineer

Chefman

Mahwah, NJ โ€ข On-site

$53 - $72.50/hr

Full-time

Re-posted 20 days ago


Job description

MLOps Engineer / DevOps Engineer

*Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

In 2020, we launched CHEF iQ, an ecosystem of connected kitchen appliances designed to transform how people cook and connect through food. Our mission is to make great cooking effortless through intelligent technology, guided experiences, and seamless integration between hardware, software, and AI. As CHEF iQ continues to expand its AI capabilities, we are building the infrastructure and platforms that will power the next generation of connected cooking experiences. From machine learning and computer vision to Generative AI applications, our success depends on scalable, reliable systems that enable rapid innovation and deployment.
ย 
We are seeking a highly detail-oriented MLOps / DevOps Engineer to serve as a critical partner to our Machine Learning Engineer, building and maintaining the cloud infrastructure, deployment pipelines, automation frameworks, and operational foundations that support AI development at scale. This individual will play a key role in improving engineering efficiency, increasing system reliability, and ensuring our AI-powered products can be developed, deployed, and scaled successfully.
ย 
The ideal candidate is passionate about automation, process improvement, and building highly scalable systems. They enjoy creating order from complexity, eliminating operational bottlenecks, and enabling teams to move faster. Experience supporting AI, machine learning, and Generative AI applications in production environments is required.
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Role and Responsibilities
  • Design, implement, and maintain scalable AWS cloud infrastructure supporting software, AI, and machine learning applications.
  • Create and manage MLOps infrastructure for model training, deployment, monitoring, versioning, and lifecycle management.
  • Partner closely with the Machine Learning Engineer to establish the tools, workflows, and infrastructure required for successful AI development and deployment.
  • Support Generative AI initiatives by building infrastructure and deployment frameworks for applications utilizing AWS Bedrock, foundation models, LLMs, and related AI services.
  • Build and manage Infrastructure as Code (IaC) using Terraform to ensure repeatable, secure, and scalable environments.
  • Implement monitoring, logging, observability, and alerting systems across software, infrastructure, and machine learning platforms.
  • Continuously identify opportunities to improve engineering processes, reduce manual effort, increase automation, and improve system reliability.
  • Develop and maintain CI/CD pipelines that enable rapid, reliable software and machine learning deployments.
  • Optimize cloud environments for scalability, performance, availability, and cost efficiency.
  • Support security, compliance, backup, disaster recovery, and operational best practices across all environments.
  • Troubleshoot infrastructure, deployment, and application issues across development, testing, and production environments.
  • Document infrastructure architecture, deployment processes, operational procedures, and engineering standards.
  • Contribute to establishing best practices for DevOps, MLOps, cloud architecture, and AI operations.
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Qualifications

Please Note: Chefman is unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

  • 5+ years of experience in MLOps, DevOps, Site Reliability Engineering (SRE), Platform Engineering, or related software engineering roles.
  • Strong hands-on experience with AWS services and cloud-native architecture.
  • Experience building and supporting AI and machine learning platforms in AWS environments.
  • Experience supporting AI, machine learning, and Generative AI applications in production environments.
  • Experience working with AWS Bedrock, Generative AI services, foundation models, LLM-powered applications, or related AI infrastructure.
  • Strong experience implementing Infrastructure as Code using Terraform.
  • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Strong experience building and maintaining CI/CD pipelines and deployment automation.
  • Experience supporting machine learning workflows, model deployment, monitoring, and MLOps platforms.
  • Strong programming and scripting skills in Python, Bash, or similar languages.
  • Experience with monitoring, logging, observability, and operational tooling.
  • Strong troubleshooting, systems-thinking, and problem-solving abilities.
  • Excellent communication and cross-functional collaboration skills.
  • Proven track record of improving engineering processes, increasing operational efficiency, and scaling software platforms.
  • Highly organized and detail-oriented with a passion for automation and continuous improvement.
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Preferred Qualifications
  • Experience with vector databases, retrieval-augmented generation (RAG), model serving, and AI infrastructure.
  • Experience supporting connected devices, IoT platforms, embedded systems, or consumer technology products.
  • Domain expertise in machine learning infrastructure, AI platforms, consumer applications, connected products, or similar technology environments.
  • Experience working in fast-paced startup or high-growth product organizations.