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Internship Tesla Machine Learning Engineer Jobs in Canton, MA

Alsym Energy

Machine Learning Engineer (Malden)

Malden, MA · On-site

$130K - $215K/yr

Base pay range $130,000.00/yr - $215,000.00/yr Direct message the job poster from Alsym Energy Alsym Energy is seeking a Machine Learning Scientist or Engineer to design, build, and deploy agentic AI ...

Capital One

Lead Machine Learning Engineer

Cambridge, MA · On-site

$112K - $147K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Capital One

Lead Machine Learning Engineer

Cambridge, MA · On-site

$112K - $147K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

S&P Global

Machine Learning Engineer II

Cambridge, MA · On-site

$106K - $145K/yr

We are seeking a mid-level Machine Learning Engineer to join our team and help shape the future of Agentic AI systems. This is a hands-on, full-lifecycle (from experimentation to productionization ...

VideaHealth

Senior Machine Learning Engineer

Boston, MA · On-site

$170K - $205K/yr

About the position: We're looking for a Senior Machine Learning Engineer with deep expertise in some area of ML engineering to join our growing ML team and work closely with our software and computer ...

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All JobsTesla Machine Learning Engineer JobsInternship Tesla Machine Learning Engineer JobsInternship Tesla Machine Learning Engineer Jobs in Canton, MA

Internship Tesla Machine Learning Engineer information

See Canton, MA salary details

$27K

$45K

$93.1K

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

As of Sep 3, 2026, the average yearly pay for internship tesla machine learning engineer in Canton, MA is $45,028.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,400.00 and $48,600.00 per year, depending on experience, location, and employer.

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 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 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 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.

Internship Tesla Machine Learning Engineer jobs near you
Infographic showing various Internship Tesla Machine Learning Engineer job openings in Canton, MA as of June 2026, with employment types broken down into 92% Full Time, 6% Part Time, and 2% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution, with an average salary of $45,028 per year, or $21.6 per hour.

Machine Learning Engineer (Malden)

Alsym Energy

Malden, MA • On-site

$130K - $215K/yr

Full-time

Re-posted yesterday

Job description

This range is provided by Alsym Energy. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$130,000.00/yr - $215,000.00/yr

Direct message the job poster from Alsym Energy

Alsym Energy is seeking a Machine Learning Scientist or Engineer to design, build, and deploy agentic AI systems that actively support scientific discovery. This role focuses on developing LLM-based agents that can autonomously retrieve proprietary experimental and simulation data, perform statistically rigorous analyses, and generate clear, traceable reports for internal scientific teams. This position sits within the Discovery, and Early Prototyping (DEP) organization and works closely with domain experts in materials science, chemistry, and energy systems. The goal is not to build a generic chatbot, but to create reliable, production-grade agents that integrate knowledge across internal data silos and materially improve how scientists reason about data. Success in this role is measured by the delivery of production-ready agentic systems actively used by scientists for data analysis and decision-making.

Core Responsibilities
  • Infrastructure Development: Design, implement, and formalize scalable agent architectures, incorporating structured prompting and advanced tool‑use (tool‑augmented reasoning).
  • Agent Development: Create LLM Agents specifically engineered to:
  • Autonomously retrieve proprietary scientific data from diverse internal systems and formats (e.g., experimental, simulation).
  • Perform relevant statistical analysis on retrieved data.
  • Produce clear, actionable reports for internal users.
  • Domain‑Expert Interaction: Collaborate with domain experts in materials science to understand data structure, define analysis requirements, and ensure statistical rigor in Agent outputs.
Required Qualifications
  • Education: Master’s or PhD in Computer Science, Machine Learning, AI, Engineering, or a related scientific field, or equivalent research/industry experience.
  • Programming: Strong programming skills in Python and knowledge of software development best practices.
  • Agentic Frameworks: Expertise in LLM frameworks (Ollama, HuggingFace Transformers) and Agent‑building toolkits (LangChain, LlamaIndex, or similar).
  • Agentic Reasoning: Proficiency in advanced LLM reasoning methods, including Chain‑of‑Thought, self‑reflection, and advanced tool‑augmented reasoning.
  • Data & Statistics: Proven ability to access, process, and perform rigorous statistical analysis on complex, proprietary scientific datasets.
  • Self‑starter and energetic.
Preferred Qualifications
  • Research experience in causal reasoning, probabilistic programming, or symbolic AI.
  • Familiarity with scientific discovery pipelines in chemistry, materials science, or energy research.
  • Experience with multimodal reasoning (combining text, image, and experimental data).
  • Practical experience in delivering and maintaining production‑ready ML/Agentic systems.
Industry Background

Alsym Energy, Inc is a leading innovator in the rapidly changing field of battery energy storage. It is essential for the world to succeed in the transition to sustainable energy and batteries are at the very core of the solution. In fact, the IEA predicts that 60% of CO₂ emission reductions in 2030 will be directly related to batteries. This mission is what drives us every day. Over the last few years, supply‑chain constraints and significant safety incidents arising from lithium‑ion batteries have reduced confidence in the suitability of the technology. Alsym’s advanced sodium ion technology enables the highest performing non‑lithium‑ion battery in the industry, based on its safe, sustainable and long‑lasting energy storage solution.

Seniority level

Mid‑Senior level

Employment type

Full‑time

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