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Internship Tesla Machine Learning Engineer Jobs in Texas

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... machine learning models and algorithms that will improve Confie's business outcome/customer experience Perform data cleansing, analysis, and feature engineering using Python Ability to work with ...

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

... Engineer Location: Austin, TX (100% Onsite) * 6 to 10 years of experience * Experience in designing and maintain the infrastructure, CI/CD pipelines, and monitoring systems that take machine learning ...

New

Engineers on the BCI team utilize signal processing and machine learning to communicate with the brain. You will have access to the most cutting-edge neural interface hardware and develop ...

Showing results 21-40

Internship Tesla Machine Learning Engineer information

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.

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 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 are the most commonly searched types of Tesla Machine Learning Engineer jobs in Texas?

The most popular types of Tesla Machine Learning Engineer jobs in Texas are:

What job categories do people searching Internship Tesla Machine Learning Engineer jobs in Texas look for?

The top searched job categories for Internship Tesla Machine Learning Engineer jobs in Texas are:

$120 - $190/hr

Other

Medical, Dental, Retirement, PTO

Posted 9 days ago


Job description

Overview

Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems. In this role, you will design, develop, and deploy state-of-the-art computer vision and language models that power scalable, real-world solutions. You’ll work with large-scale image and video data, building and optimizing production-grade vision systems while contributing clean and modular code to shared repositories.

As part of our AI team, you’ll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings hands-on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.

Responsibilities
  • Fine-tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
  • Develop vision-language models and Mixture of Experts architectures, from experimental design through production deployment.
  • Architect Retrieval-Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
  • Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
  • Contribute to our machine learning repositories and optimize models for performance, scalability, and real-time inference across edge and cloud environments.
  • Drive performance optimization and scalability of ML systems across edge and cloud environments.
  • Collaborate with cross-functional teams to integrate computer vision solutions into end-to-end products, translating research outcomes into measurable platform impact.

This list of responsibilities might not cover everything you’ll end up doing.

Qualifications
  • 5+ years of hands-on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
  • Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
  • Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
  • Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
  • Experience building and deploying LLM-based systems and Retrieval-Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
  • Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.
Benefits

We offer a Total Rewards plan designed with you and your family’s health and wellness in mind that includes:

  • Paid days off (i.e. vacation, sick days, bereavement leave)
  • Health and Dental plans
  • Retirement plans
  • Employee and Family Assistance Program (EFAP)
  • Employee referral program

We welcome applicants from all backgrounds, regardless of race, color, religion, sex, veteran status, sexual orientation, gender identity, national origin, age, or disabilit y or any other protected characteristics in accordance with applicable federal, state/provincial, and local laws. We’re committed to creating a workplace where everyone feels valued and respected.

We appreciate all responses and will acknowledge only those being considered for an interview.

We respectfully request no calls or unsolicited resumes from Agencies.

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