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Jr Machine Learning Engineer Jobs in Vernon, NJ (NOW HIRING)

Chefman

MLOps Engineer / DevOps Engineer

Mahwah, NJ · On-site

$53 - $72.50/hr

MLOps Engineer / DevOps Engineer *Candidates must be legally authorized to work in the United ... From machine learning and computer vision to Generative AI applications, our success depends on ...

Chefman

MLOps Engineer / DevOps Engineer

Mahwah, NJ

$53 - $72.50/hr

MLOps Engineer / DevOps Engineer *Candidates must be legally authorized to work in the United ... From machine learning and computer vision to Generative AI applications, our success depends on ...

Mindlance

Data Scientist

Franklin Lakes, NJ · On-site

They are seeking a Data Scientist to work on data extraction, processing, and applying machine learning techniques to large datasets. The role requires strong programming skills and the ability to ...

Keylent Inc

RPA WorkFusion Lead

Branchville, NJ · On-site

... Machine Learning, Workflows, Backend and APIs. • Knowledge on Technical Documentation and ... Client Engineer, Client Data Analyst. • Articulate and explain the architecture options to a ...

Ghostwerks LLC

Data base Manager

Wharton, NJ · On-site

Containerization and DevOps: * Demonstrated expertise in using Docker to containerize applications ... AI & Machine Learning Integration: * Familiarity with integrating Artificial Intelligence (AI) and ...

Motorola

Director of Computer Vision

Florida, NY · On-site

Our AI-powered security solutions integrate advanced video analytics, machine learning, and ... Technical Vision, Engineering Leadership, and Execution: Provide executive technical leadership to ...

Thorlabs

Analytics Engineering Supervisor

Newton, NJ · On-site

The Analytics Engineering Supervisor is responsible for partnering with business and IT ... machine learning, by enforcing consistent data definitions, governance practices, and reusable ...

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All JobsJr Machine Learning Engineer JobsJr Machine Learning Engineer Jobs in Vernon, NJ

Jr Machine Learning Engineer information

See Vernon, NJ salary details

$32.6K

$69.8K

$106.5K

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

As of Sep 15, 2026, the average yearly pay for jr machine learning engineer in Vernon, NJ is $69,816.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,200.00 and $77,800.00 per year, depending on experience, location, and employer.

What does a Jr Machine Learning Engineer do?

A Jr Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the guidance of more senior engineers or data scientists. Their responsibilities often include data preprocessing, feature engineering, model training, testing, and helping to integrate models into production systems. They also work on debugging issues, documenting code, and staying up-to-date with the latest industry trends and tools. Junior engineers typically collaborate closely with cross-functional teams to deliver AI-powered solutions.

What are the key skills and qualifications needed to thrive as a Jr Machine Learning Engineer?

To thrive as a Jr Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, as well as experience with data preprocessing and version control systems, is typically required. Strong analytical thinking, problem-solving skills, and the ability to collaborate effectively help you stand out in this role. These competencies are crucial for developing, optimizing, and deploying machine learning models that address real-world business challenges.

What are some common challenges faced by Jr Machine Learning Engineers in their first year on the job?

Jr Machine Learning Engineers often encounter challenges such as understanding complex codebases, managing large datasets, and bridging the gap between academic concepts and real-world applications. Collaboration with data scientists, software engineers, and product teams can also be a learning curve, as effective communication is crucial for project success. Additionally, balancing tasks like model development, testing, and deployment within fast-paced agile environments can be demanding, but these experiences provide valuable opportunities for skill growth and professional development.

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

AspectJr Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often with advanced certifications
Work EnvironmentFocus on developing and deploying ML models, coding, and data preprocessingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, consulting, research institutions

The Jr Machine Learning Engineer primarily develops and deploys ML models, requiring coding skills and familiarity with ML frameworks. Data Scientists analyze data, build statistical models, and interpret insights. While both roles work with data, the Jr Machine Learning Engineer is more focused on implementation, whereas Data Scientists focus on analysis and strategy.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, industry, and experience. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.

What cities near Vernon, NJ are hiring for Jr Machine Learning Engineer jobs?

Cities near Vernon, NJ with the most Jr Machine Learning Engineer job openings:

Jr Machine Learning Engineer jobs near you
Infographic showing various Jr Machine Learning Engineer job openings in Vernon, NJ as of September 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $69,816 per year, or $33.6 per hour.

MLOps Engineer / DevOps Engineer

Mahwah, NJ • On-site

Chefman
Manufacturing • 51 - 200 employees
Learn more about Chefmanexternal

$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.
 
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.
 
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.
 
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.
 
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.
 
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.

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