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Internship Edge Ai Machine Learning Jobs (NOW HIRING)

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 Months Contract Job Summary We are seeking an experienced AI / Machine Learning Engineer to design ...

Fireworks AI

Applied Machine Learning Engineer

New York, NY · On-site

$170K - $240K/yr

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

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How much do internship edge ai machine learning jobs pay per year?

As of Jun 29, 2026, the average yearly pay for internship edge ai machine learning 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 Internship Edge AI Machine Learning positions?

Internship Edge AI Machine Learning positions are entry-level roles designed for students or recent graduates who want hands-on experience working with artificial intelligence and machine learning technologies, especially those related to 'edge' computing. These internships focus on developing, optimizing, and deploying AI/ML models that run on edge devices such as smartphones, IoT devices, and embedded systems, rather than in the cloud. Interns typically assist in research, data preparation, model training, and software development while gaining industry-relevant skills. These roles are ideal for individuals interested in both hardware and software aspects of AI. The experience gained can be valuable for future careers in data science, machine learning engineering, or AI research.

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

To thrive in an Internship Edge AI Machine Learning role, you need a solid background in computer science, mathematics, and machine learning concepts, typically supported by coursework or relevant projects. Familiarity with programming languages like Python, machine learning libraries (such as TensorFlow or PyTorch), and cloud or edge computing platforms is highly valuable. Strong analytical thinking, problem-solving abilities, and effective teamwork skills help you adapt and contribute meaningfully in collaborative research and development environments. These competencies are crucial for innovating and deploying machine learning models on edge devices, ensuring impactful real-world AI solutions.

What is the difference between Internship Edge Ai Machine Learning vs Data Analyst?

AspectInternship Edge Ai Machine LearningData Analyst
Required CredentialsRelevant coursework, basic programming skills, possibly some certificationsDegree in statistics, mathematics, or related field; proficiency in data tools
Work EnvironmentInternship setting, collaborative teams, research-focusedOffice environment, data-driven decision-making teams
Employer & Industry UsageTech companies, startups, research institutionsBusiness, finance, healthcare, marketing sectors

Internship Edge Ai Machine Learning roles typically focus on foundational skills in AI and machine learning, often as entry-level or internship positions. Data Analysts work across various industries analyzing data to inform business decisions. While both roles involve working with data, AI internships emphasize machine learning models, whereas Data Analysts focus on data interpretation and reporting.

What types of projects can I expect to work on during an Internship in Edge AI and Machine Learning?

As an intern in Edge AI and Machine Learning, you will likely work on projects that involve developing and optimizing machine learning models for deployment on edge devices such as smartphones, IoT sensors, or embedded systems. Typical tasks include data preprocessing, model training and evaluation, and implementing algorithms with resource constraints in mind. You may also collaborate with hardware engineers and software developers to ensure that your solutions run efficiently on limited hardware. This hands-on experience provides a strong foundation for understanding real-world AI deployment challenges and can open doors to more advanced roles in the future.
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Internship Edge Ai Machine Learning jobs near you
Infographic showing various Internship Edge Ai Machine Learning job openings in the United States as of June 2026, with employment types broken down into 95% Full Time, 3% Part Time, 1% Temporary, and 1% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Applied Machine Learning Engineer

Fireworks AI

New York, NY

Other

Posted 11 days ago

Key responsibilities

  • Collaborate with the GTM team to ensure smooth integration and successful deployment of machine learning solutions.

  • Build and present proof of concepts that demonstrate the capabilities of AI technology.

  • Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.

Job description

The Role:

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions.

Key Responsibilities:
  • Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions.
  • Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology.
  • Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.
  • Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues.
  • New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments.
  • Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications.
  • Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration.
Minimum Qualifications:
  • Bachelor's degree in Computer Science, Engineering, or a related technical field.
  • 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles.
  • Robust coding skills required, preferably with proficiency in Python.
  • Demonstrated ability to lead and execute complex technical projects with a focus on customer success.
  • Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams.
Preferred Qualifications:
  • Master's degree in Computer Science, Engineering, or a related technical field.
  • Experience working in a startup or fast-paced environment.
  • Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT).
  • Solid understanding of generative AI, machine learning principles, and enterprise infrastructure.

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