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Machine Learning Engineer I Jobs (NOW HIRING)

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

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 ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains.

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

As of May 29, 2026, the average yearly pay for machine learning engineer i in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer I, and why are they important?

To thrive as a Machine Learning Engineer I, you need a solid foundation in programming (especially Python), mathematics, and machine learning concepts, typically supported by a bachelor’s degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, version control systems (e.g., Git), and cloud platforms is often expected. Strong problem-solving abilities, teamwork, and effective communication help you collaborate with stakeholders and translate business needs into technical solutions. These skills are crucial for building robust models, integrating them into production environments, and driving impactful results in data-driven projects.

What are the typical projects a Machine Learning Engineer I can expect to work on during their first year?

As a Machine Learning Engineer I, you can expect to work on projects such as data preprocessing, building and testing basic machine learning models, and implementing existing algorithms under the guidance of senior team members. You'll often collaborate with data scientists, software engineers, and product managers to translate business requirements into technical solutions. Early projects may also involve model evaluation, feature engineering, and helping to deploy models into production environments. This hands-on experience helps build a strong foundation for tackling more complex problems as you advance in your career.

What are Machine Learning Engineer I?

A Machine Learning Engineer I is an entry-level professional who designs, builds, and deploys machine learning models within software applications. They work closely with data scientists and software developers to implement algorithms that allow computers to learn from data and make predictions or decisions. Typical responsibilities include cleaning and preparing data, training models, evaluating performance, and optimizing algorithms for scalability and efficiency. This role often requires knowledge of programming languages like Python, frameworks such as TensorFlow or PyTorch, and a solid understanding of statistics and machine learning principles.

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

AspectMachine Learning Engineer IData Scientist
Required CredentialsBachelor's in CS, Math, or related field; some roles may prefer certifications in ML or data analysisBachelor's or higher in Statistics, Data Science, or related field; often requires knowledge of programming and statistics
Work EnvironmentDevelops, tests, and deploys ML models; collaborates with data engineers and software developersAnalyzes data, builds models, and provides insights; works closely with business teams and analysts
Employer & Industry UsageTech companies, startups, and industries implementing AI solutionsFinance, healthcare, marketing, and research sectors relying on data-driven decisions

Machine Learning Engineer I focuses on developing and deploying ML models, while Data Scientists analyze data to generate insights. Both roles require programming skills and a background in math or statistics, but their daily tasks and objectives differ slightly.

More about Machine Learning Engineer I jobs
What cities are hiring for Machine Learning Engineer I jobs? Cities with the most Machine Learning Engineer I job openings:
What states have the most Machine Learning Engineer I jobs? States with the most job openings for Machine Learning Engineer I jobs include:
What job categories do people searching Machine Learning Engineer I jobs look for? The top searched job categories for Machine Learning Engineer I jobs are:
Machine Learning Engineer I jobs near you
Infographic showing various Machine Learning Engineer I job openings in the United States as of May 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Purple Drive Technologies

Newark, NJ • On-site

Full-time

Retirement

Posted 28 days ago

Job description

Overview:
Role: Machine Learning Engineer
As a Machine Learning Engineer, you will play a critical role in designing, developing, and deploying advanced machine learning solutions that drive innovation and create measurable business value. You will collaborate closely with business stakeholders, data scientists, and cross-functional engineering teams to transform ideas into scalable, production-ready systems. This role requires both strong technical expertise and leadership ability to guide a small team and deliver impactful solutions.
Key Responsibilities
  • Lead and deliver machine learning projects from inception through production, building strong relationships with business partners and cross-functional teams.
  • Collaborate with business leaders and subject matter experts to define success criteria and optimize products, features, and models.
  • Partner with data scientists to design, implement, and train machine learning models.
  • Work with infrastructure teams to enhance architecture, scalability, stability, and performance of ML platforms.
  • Design and optimize data pipelines to support high-performance ML model training and inference.
  • Extend and customize machine learning libraries and frameworks for project-specific needs.
  • Define and implement model monitoring, governance, and operationalization processes for ML solutions.
  • Establish objectives and own the technical roadmap for ML platforms, ensuring delivery of results.
  • Define and promote standards of engineering and operational excellence for ML systems.
  • Apply architectural best practices in the delivery of data science and AI solutions.
Required Skills & Experience
  • Strong background in software engineering with proven experience as a Machine Learning Engineer.
  • Bachelor's degree in Computer Science, Computer Engineering, or related field; Master's preferred.
  • Advanced proficiency in Python, Java, and Scala with solid CS fundamentals (algorithms, data structures, multithreading).
  • Hands-on experience with Generative AI, LangChain, and RAG-based techniques.
  • Expertise in ML/DL libraries such as XGBoost, Scikit-learn, TensorFlow, and PyTorch.
  • Experience building and deploying ML solutions on public clouds (AWS, GCP).
  • Familiarity with ML platforms such as SageMaker, H2O, and DataRobot.
  • Strong knowledge of the ML lifecycle, including containerization, batch vs. real-time inference, and application security.
  • Proven track record in developing and deploying production-grade ML applications with cloud-based automation pipelines.
  • Experience working in Agile/Scrum environments with multiple stakeholders.
  • Excellent communication, collaboration, and problem-solving skills with thought leadership and innovative thinking.
Nice-to-Have
  • Experience with search platforms (e.g., Solr, Elasticsearch).
  • Hands-on experience building recommender systems.
  • Exposure to graph databases (e.g., Neo4j).
  • Familiarity with CI/CD tools (e.g., Jenkins).
  • Domain knowledge in Financial Services, Insurance, or 401K.
  • AWS Solutions Architect certification.

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