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Embedded Machine Learning Internship Jobs in New Port Richey, FL

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Embedded Machine Learning Internship information

See New Port Richey, FL salary details

$22.7K

$37.9K

$78.4K

How much do embedded machine learning internship jobs pay per year?

As of Jun 8, 2026, the average yearly pay for embedded machine learning internship in New Port Richey, FL is $37,929.00, according to ZipRecruiter salary data. Most workers in this role earn between $28,900.00 and $41,000.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning Internship?

An Embedded Machine Learning Internship is a temporary position designed for students or recent graduates to gain hands-on experience in developing and deploying machine learning algorithms on embedded systems. These internships typically involve working with hardware such as microcontrollers, sensors, or edge devices, and using specialized tools to optimize machine learning models for low-power and resource-constrained environments. Interns collaborate with engineers and data scientists to create efficient, real-world AI solutions that run directly on devices rather than relying on cloud computing. This role helps bridge the gap between theoretical machine learning concepts and practical implementation on embedded platforms.

What are some typical projects or tasks I might work on during an Embedded Machine Learning Internship?

During an Embedded Machine Learning Internship, you can expect to work on projects such as optimizing machine learning models to run efficiently on hardware with limited resources, integrating AI algorithms into embedded systems (like microcontrollers or IoT devices), and performing real-time data processing. You'll likely collaborate closely with software engineers and hardware designers to test models on physical devices, debug performance issues, and contribute to documentation. These experiences provide practical exposure to the challenges of deploying AI in real-world, resource-constrained environments and help build skills valuable for a future career in embedded AI.

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Intern, and why are they important?

To thrive as an Embedded Machine Learning Intern, you need a background in computer science, electrical engineering, or a related field with strong programming skills in C/C++ and Python, as well as foundational knowledge of machine learning algorithms. Experience with embedded systems development tools (such as ARM Cortex, Raspberry Pi, or Arduino), version control systems, and familiarity with ML frameworks like TensorFlow Lite or Edge Impulse is often required. Analytical thinking, problem-solving ability, and effective teamwork are vital soft skills for success in this role. These skills and qualities are crucial for efficiently developing, optimizing, and deploying machine learning solutions on resource-constrained embedded platforms.
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Embedded Machine Learning Internship jobs near you
Infographic showing various Embedded Machine Learning Internship job openings in New Port Richey, FL as of May 2026, with employment types broken down into 72% Full Time, and 28% Part Time. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $37,929 per year, or $18.2 per hour.
Technical Advisor SME - Clearance Required

Technical Advisor SME - Clearance Required

Logistics Management Institute

Tampa, FL • On-site

$138K - $238K/yr

Other

Posted 23 days ago

Job description

Overview
LMI is seeking a Technical Advisor SME in the Tampa area who is a senior-level Subject Matter Expert (SME) in Artificial Intelligence and Autonomy to shape the strategic direction of unmanned systems across the Special Operations enterprise.
This role sits at the intersection of strategic planning and deep technical fluency - translating operational requirements into actionable AI/autonomy strategies, evaluating emerging technologies, and aiding decision makers as a voice on intelligent unmanned systems to industry, academia, interagency, and allied partners.
The ideal candidate bridges the gap between operators, engineers, and senior decision-makers - equally credible briefing a general officer on capability roadmaps and challenging an engineering team on model architectures, sensor fusion approaches, or autonomy frameworks.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.
Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors-helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
This position is in-person in Tampa and requires an active Top-Secret clearance.
Responsibilities
As a Technical Advisor SME you are expected to:
Develop and refine USSOCOM's strategic approach to AI and autonomy for unmanned systems (air, ground, maritime, and undersea), ensuring alignment with the National Defense Strategy, Joint All-Domain Command and Control (JADC2), and SOF-specific operational concepts.
Advise senior leadership (O-6 and above) on capability gaps, technology readiness, and investment priorities related to AI-enabled unmanned platforms and autonomous mission sets.
Evaluate and challenge proposals from defense industry, academic research institutions, and government labs on AI/ML architectures, autonomy stacks, computer vision, reinforcement learning, edge computing, and human-machine teaming approaches.
Lead technical assessments of unmanned system prototypes and programs of record, including autonomy software, navigation/guidance algorithms, multi-agent coordination, and mission-level decision-making systems.
Shape requirements documents, Concepts of Operations (CONOPS), and capability development documents (CDDs) to ensure AI/autonomy considerations are embedded from inception.
Represent USSOCOM in interagency working groups, Joint AI Center (JAIC) / CDAO engagements, NATO autonomy forums, and industry conferences.
Identify opportunities for rapid prototyping, experimentation, and transition of AI/autonomy technologies from lab to operational fielding, leveraging authorities such as Section 804 and SOFWERX partnerships.
Monitor the global AI/autonomy landscape - including adversary capabilities, allied developments, and commercial breakthroughs - and translate implications into actionable intelligence for SOCOM leadership.
Qualifications
  • Active Top-Secret Clearance required
  • Minimum of 10-years of military experience with special operations time
  • Bachelor's degree
  • Ability to communicate at the SES/GO level
  • Demonstrated experience across technology delivery in areas similar to: software development, DevSecOps, Cloud Computing, AI, data management, data science, and networking
  • Experience in special operations, ideally in both a joint and US Army SOF unit
  • Ability to function with little or no guidance in a fast faced technical operations environment

Desired Qualifications
  • Master's degree in a technical field
  • Prior service in a Special Operations unit

Target salary range: $138,130 - $238,118
Disclaimer:
The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.
Applicants must meet eligibility requirements for a U.S. Government security clearance. Only US Citizens are eligible for a security clearance. For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work.

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