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Internship Tesla Machine Learning Engineer Jobs (NOW HIRING)

Machine Learning Engineer Location: Fremont, CA Duration: 12+ Months Visa: No GC LinkedIn : Must with photo or match project Year exp only 12 not more than that Interview F2F About the Role: * Our ...

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Machine Learning Engineer

Mclean, VA · On-site +1

$115K - $150K/yr

We are looking for a more than just a "Machine Learning Engineer", but a technologist with excellent communication and customer service skills and a passion for data and problem solving.

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

We are looking for seasoned Machine Learning Engineer to work with our existing team of Data Scientists and Engineers to use AI/ML technology in supporting Federal use cases. We are looking for a ...

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation ...

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 ...

About the Role We are seeking a skilled and innovative Machine Learning Engineer to join our team. This person will implement and develop machine learning models to enhance our platform ...

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...

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Internship Tesla Machine Learning Engineer information

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$25.5K

$42.6K

$88K

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

As of Jun 1, 2026, the average yearly pay for internship tesla machine learning engineer in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Internship Tesla Machine Learning Engineer, and why are they important?

To thrive as an Internship Tesla Machine Learning Engineer, you need a solid background in computer science, mathematics, and machine learning principles, often supported by progress toward a relevant bachelor’s or master’s degree. Familiarity with Python, TensorFlow or PyTorch, and experience using data processing tools and version control systems are typically required. Strong problem-solving, communication skills, and the ability to collaborate effectively in a fast-paced team environment will set you apart. These skills and qualities are crucial for contributing to high-impact projects and advancing cutting-edge AI solutions at Tesla.

What types of projects do Machine Learning Engineer interns at Tesla typically work on, and how much ownership do they have over their work?

Machine Learning Engineer interns at Tesla are often involved in projects that directly contribute to the development of advanced AI systems, such as autonomous driving, predictive analytics, or manufacturing optimization. Interns are typically given meaningful, hands-on tasks and are expected to take significant ownership of specific components or models within a larger project. Collaboration with senior engineers and cross-functional teams is common, providing exposure to Tesla's fast-paced, innovative work culture. Interns also have opportunities to present their work to leadership and receive mentorship, which can be valuable for future career growth.

What does an Internship Tesla Machine Learning Engineer do?

An Internship Tesla Machine Learning Engineer assists in developing and improving machine learning models used in Tesla’s products and operations. Interns typically work on data preprocessing, algorithm development, and model evaluation under the guidance of senior engineers. Their projects may involve computer vision, natural language processing, or predictive analytics applied to Tesla’s vehicles, manufacturing, or autonomous driving systems. The internship offers hands-on experience with real-world data and cutting-edge technology, helping students build valuable industry skills.

What is the difference between Internship Tesla Machine Learning Engineer vs Data Scientist Intern?

AspectInternship Tesla Machine Learning EngineerData Scientist Intern
Required CredentialsRelevant coursework, programming skills, possibly some machine learning knowledgeStatistics, data analysis, programming skills, often some machine learning understanding
Work EnvironmentHands-on projects in AI/ML teams at Tesla, collaborative, fast-pacedData analysis tasks, reporting, modeling in various departments, collaborative
Employer & Industry UsageTesla, automotive, AI, and autonomous driving sectorsVarious industries including tech, finance, healthcare, often within data teams

Both roles involve data and programming skills, but the Tesla Machine Learning Engineer internship focuses more on developing AI/ML models for autonomous systems, while Data Scientist Internships typically emphasize data analysis and insights across different business areas.

More about Internship Tesla Machine Learning Engineer jobs
What cities are hiring for Internship Tesla Machine Learning Engineer jobs? Cities with the most Internship Tesla Machine Learning Engineer job openings:
What are the most commonly searched types of Tesla Machine Learning Engineer jobs? The most popular types of Tesla Machine Learning Engineer jobs are:
What states have the most Internship Tesla Machine Learning Engineer jobs? States with the most job openings for Internship Tesla Machine Learning Engineer jobs include:
Infographic showing various Internship Tesla Machine Learning Engineer job openings in the United States as of May 2026, with employment types broken down into 55% Full Time, 42% Part Time, and 3% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

AI Squared

Washington, DC

Full-time

Posted 10 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.