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Embedded Machine Learning Internship Jobs in Rochester, NY

D3 Engineering

Engineering Program Manager

Rochester, NY · On-site

$101.10 - $146.70/hr

... machine learning. This position requires strong problem-solving skills, the ability to mentor and guide engineering teams. The Responsibilities * Lead a project team to develop an embedded system and ...

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Embedded Machine Learning Internship information

See Rochester, NY salary details

$25.2K

$42K

$86.8K

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

As of Aug 17, 2026, the average yearly pay for embedded machine learning internship in Rochester, NY is $42,016.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,100.00 and $45,400.00 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

Global Quantitative Strategies | Machine Learning Engineer

Citadel LLC

Rochester, NY • On-site

$275 - $350/hr

Other

Medical, Life, Retirement

Posted 11 days ago

Job description

Overview

Global Quantitative Strategies (GQS) is the quantitative investment business of Citadel. Founded in 2012, GQS has grown into one of Citadel’s core investment strategies and one of the top quantitative investment teams in the world. Collaborative teams of researchers, engineers, and traders develop robust systems and advanced quantitative models to operate at scale and identify investment opportunities across global markets.

Machine Learning Engineers (MLEs) in GQS work at the intersection of deep learning, quantitative research, and high-performance computing. In this role, you will collaborate closely with Quantitative Researchers and Quantitative Research Engineers to design, build, optimize, and scale models and modeling systems that power research and production workflows. This is not a traditional infrastructure engineering role. MLEs are deeply embedded in the research process, partnering with researchers to understand modeling challenges, translate research ideas into scalable model architectures, and improve the performance, reliability, and efficiency of machine learning systems.

You will work on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries that enable the development and deployment of models across major asset products globally. The work directly supports the research and productionization of machine learning models used in systematic investing, including developing new modeling approaches, optimizing large-scale training workflows, and creating tools that help researchers experiment faster and more effectively.

Responsibilities

Design, implement, and optimize machine learning models and modeling systems used in research and production workflows.

Collaborate with Quantitative Researchers and Engineers to translate research ideas into scalable model architectures.

Contribute to distributed training, inference optimization, and tooling for ML libraries used across the firm.

Develop and optimize ML workflows for training speed, inference performance, scalability, reliability, and cost efficiency.

Work within Linux-based, high-performance computing or distributed computing environments and ensure robust, maintainable solutions.

Qualifications
  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or an equivalent technical field
  • Strong programming skills in Python with experience in C++, CUDA, or other performance-oriented technologies
  • Experience designing, implementing, training, or optimizing machine learning models, particularly deep learning models
  • Strong understanding of model architecture, training dynamics, optimization techniques, and performance tradeoffs
  • Experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
  • Experience building or extending ML libraries, research tooling, model training systems, or distributed training workflows
  • Ability to optimize ML workflows for training speed, inference performance, scalability, reliability, and cost efficiency
  • Experience developing on a Linux stack and working in modern HPC or distributed computing environments
  • Ability to collaborate with researchers, understand open-ended research problems, and translate modeling needs into robust technical solutions
  • Proven track record of solving complex technical problems with creativity, strong judgment, and attention to research impact
  • Strong communication skills and ability to work across research, engineering, and infrastructure teams
  • Interest in financial markets and applying ML to systematic investing
Privacy and Compliance

We collect and use personal data in accordance with our Privacy Policy. We retain data on prospective candidates and may consider suitability for alternative opportunities at Citadel. For more information, see our Privacy Policy.

Compensation and Benefits

In accordance with applicable law, the base salary range for this role is $275,000 to $350,000. The employee in this role will be eligible to participate in a discretionary incentive compensation program, as well as a wide array of benefit programs, including medical and life insurance, retirement and tax-free savings plans, and access to other healthcare programs.

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