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Internship Machine Learning Engineer New Grad Jobs in Murrieta, CA

... Engineering teams. You Should Have * A bachelor's degree completed within the last 12 months, or ... Strong problem-solving skills and comfort learning new processes quickly in a fast-paced, sometimes ...

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... Engineering teams. You Should Have * A bachelor's degree completed within the last 12 months, or ... Strong problem-solving skills and comfort learning new processes quickly in a fast-paced, sometimes ...

Posted today

Work alongside senior engineers to design and implement innovative features * Stay informed about ... machine learning, computer hardware, and the business of software, to enhance your contributions to ...

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Internship Machine Learning Engineer New Grad information

See Murrieta, CA salary details

$26.1K

$43.5K

$90K

How much do internship machine learning engineer new grad jobs pay per year?

As of Sep 2, 2026, the average yearly pay for internship machine learning engineer new grad in Murrieta, CA is $43,537.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,200.00 and $47,000.00 per year, depending on experience, location, and employer.

What does an internship machine learning engineer new grad do?

An Internship Machine Learning Engineer New Grad typically works on developing, testing, and optimizing machine learning models under the guidance of senior engineers or data scientists. Their responsibilities often include data preprocessing, feature engineering, model training, and evaluating model performance. They may also collaborate with cross-functional teams to integrate models into production or contribute to research projects. This role provides hands-on experience with real-world data and the opportunity to learn industry-standard tools and practices.

What are the key skills and qualifications needed to thrive as an internship machine learning engineer new grad?

To thrive as an Internship Machine Learning Engineer New Grad, you need a strong grasp of programming (especially Python), machine learning algorithms, data structures, and a relevant degree or coursework in computer science or a related field. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is typically expected. Strong analytical thinking, problem-solving abilities, and a willingness to learn make you stand out in this position. These skills enable you to contribute effectively to projects, quickly adapt to new challenges, and support innovative solutions in a fast-evolving field.

What types of projects do machine learning engineer interns typically work on?

Machine Learning Engineer interns often work on hands-on projects such as data preprocessing, model development, and conducting experiments to validate algorithms under the guidance of senior engineers. These projects might include building prototypes, optimizing existing machine learning models, or supporting data collection and annotation efforts. Interns are expected to collaborate closely with data scientists, software engineers, and product teams to align their work with real business needs. This experience not only helps interns build technical skills but also provides insight into how machine learning solutions are integrated into larger products or services.

What is the difference between Internship Machine Learning Engineer New Grad vs Machine Learning Engineer?

AspectInternship Machine Learning Engineer New GradMachine Learning Engineer
Required CredentialsTypically pursuing or recently completed a Bachelor's or Master's in CS, Data Science, or related fieldsBachelor's or higher in CS, Data Science, or related fields; often requires some professional experience
Work EnvironmentTemporary, learning-focused internship, often part-time or summerFull-time professional role in a team, responsible for deploying ML models and projects
Employer & Industry UsageInternships offered by tech companies, startups, and research labs; industry-wideFull-time roles in tech, finance, healthcare, and other sectors utilizing ML

The main difference between an Internship Machine Learning Engineer New Grad and a Machine Learning Engineer is experience level and job responsibilities. Internships are temporary, learning-focused positions for recent graduates or students, while full-time Machine Learning Engineers handle ongoing projects, deployment, and optimization of ML models in a professional setting.

What are popular job titles related to Internship Machine Learning Engineer New Grad jobs in Murrieta, CA?

For Internship Machine Learning Engineer New Grad jobs in Murrieta, CA, the most frequently searched job titles are:

What cities near Murrieta, CA are hiring for Internship Machine Learning Engineer New Grad jobs?

Cities near Murrieta, CA with the most Internship Machine Learning Engineer New Grad job openings:

Infographic showing various Internship Machine Learning Engineer New Grad job openings in Murrieta, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $43,537 per year, or $20.9 per hour.

1.6 Machine Learning Operations Engineer (Mission Viejo)

Field AI

Mission Viejo, CA • On-site

$70K - $300K/yr

Full-time

Re-posted yesterday


Job description

1 week ago Be among the first 25 applicants

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

As an MLOps Engineer at Field AI, you will play a pivotal role in ensuring the scalability, efficiency, and reliability of our machine learning systems. Our company is at the forefront of robotics innovation, with a global fleet of robots generating vast amounts of data. Your work will directly impact how we manage and utilize this data to optimize the performance of our robots and drive innovation across industries.

You will work alongside a collaborative team of data scientists, software engineers, and robotics experts, helping to bridge the gap between machine learning models and production systems. While your primary focus will be on developing and maintaining robust ML infrastructure and pipelines, you will also assist with model deployment, ensuring that models are integrated smoothly and perform optimally in live environments.

This role offers the opportunity to work with cutting-edge technologies, solve complex problems, and contribute to the success of large-scale, real-time data systems. You’ll be key in managing large data flows, and ensuring that our robots continue to operate seamlessly and efficiently worldwide.

What You’ll Get To Do
  • Machine Learning Infrastructure & Data Pipelines
  • Collaborate with data scientists and software engineers to design and build scalable machine learning infrastructure that supports the data generated by our global robot fleet
  • Manage and optimize large-scale data pipelines that handle continuous streams of data from robots deployed worldwide
  • Develop and implement strategies for model versioning, reproducibility, and efficient retraining workflows
  • Leverage cloud infrastructure (AWS, Azure, GCP) to support model training, deployment, and monitoring at scale
  • Model Deployment, Monitoring & Performance
  • Assist with deploying machine learning models into production environments, working closely with the data science team to ensure smooth integration and performance
  • Automate and streamline the monitoring and maintenance of machine learning models in production
  • Continuously monitor models in production, detecting model drift and automating retraining processes as necessary
  • Troubleshoot issues related to model deployment, performance, and system integration
  • Systems Optimization & Troubleshooting
  • Ensure seamless integration of machine learning models into production systems, optimizing for scalability, reliability, and performance
  • Work to identify and resolve complex system performance issues related to model deployments, data pipelines, and cloud infrastructure
  • Support the development of system architecture strategies to improve ML model deployment workflows and cloud infrastructure performance
  • Maintain and optimize CI/CD pipelines for machine learning workflows to ensure continuous delivery of reliable models
What You Have
  • 3+ years of relevant experience in MLOps, DevOps, or a similar role, preferably within a robotics or data-intensive environment
  • Strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
  • 3+ years of hands-on experience with containerization (e.g., Docker, Kubernetes) and orchestration tools
  • Familiarity with cloud-based platforms for machine learning (AWS, Azure, GCP)
  • Experience with building and maintaining CI/CD pipelines for machine learning workflows
  • Proficiency in version control tools such as Git
  • Strong understanding of system architecture, software development practices, and how they relate to ML model deployment
What Will Set You Apart
  • Experience working with large-scale data systems, particularly those involving real-time data streams from sensors and robots
  • Familiarity with ML deployment platforms such as Weight and Bias, MLflow, Kubeflow, or similar
  • Strong knowledge of monitoring tools and logging platforms for real-time model and system performance analysis

Compensation and Benefits

Our salary range is generous ($70,000 - $300,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option.

Why Join Field AI?

We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.

You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.

Be Part of the Next Robotics Revolution

To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.

We are headquartered in always-sunny Mission Viejo (Irvine adjacent), Southern California and have US based and global teammates.

Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!

We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, martial status, mental or physical disability, or any other legally protected status.

Seniority level
  • Not Applicable
Employment type
  • Full-time
Job function
  • Engineering and Information Technology
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