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Jr Machine Learning Engineer Jobs (NOW HIRING)

innovitusa

Machine Learning Engineer

Atlanta, GA

We are seeking a Machine Learning Engineer to develop, deploy, and optimize machine learning models and AI solutions. The ideal candidate will have strong experience with Python, machine learning ...

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

AI/Machine Learning Engineer This project-based consulting role invites an experienced Machine Learning Engineer to apply advanced analytical, statistical, and software engineering expertise to ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

Givzey

Machine Learning Engineer

Machine Learning Engineer We're looking for a talented and motivated Machine Learning Engineer to join our team and help develop cutting-edge AI solutions. In this role, you'll have the opportunity ...

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Showing results 21-40

All JobsJr Machine Learning Engineer Jobs

Jr Machine Learning Engineer information

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$33.5K

$71.8K

$109.5K

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 the United States is $71,799.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,500.00 and $80,000.00 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.
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Infographic showing various Jr Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution, with an average salary of $71,799 per year, or $34.5 per hour.

Machine Learning Engineer

Washington, DC

AI Squared
See more jobs at AI Squaredexternal

Full-time

Re-posted 26 days ago

Job description

Machine Learning Engineer
Washington, DC (Hybrid)

About the Role:

We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities:
  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.
Qualifications:
  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.