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Internship Tesla Machine Learning Engineer Jobs in Nebraska

Bespoke Labs

Machine Learning Engineer

Kearney, NE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Omaha, NE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Lincoln, NE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Bellevue, NE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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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 job categories do people searching Internship Tesla Machine Learning Engineer jobs in Nebraska look for? The top searched job categories for Internship Tesla Machine Learning Engineer jobs in Nebraska are:
What cities in Nebraska are hiring for Internship Tesla Machine Learning Engineer jobs? Cities in Nebraska with the most Internship Tesla Machine Learning Engineer job openings:
Internship Tesla Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Grand Island, NE • On-site

Full-time

Posted 8 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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