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Phd Electrical Engineering Machine Learning Jobs

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

$165K - $200K/yr

Required * BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field. * Strong hands-on programming experience in C++ and ...

Cisco Systems, Inc.

WI · On-site

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Machine Learning, Artificial Intelligence, or a closely related technical field * Programming experience in C++ or Python ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

PhD or MS in Computer Science and Electrical Engineering * Expert level coding skills (Python, C++ at minimum) * 3+ years' experience working with machine learning in embedded applications: model ...

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Phd Electrical Engineering Machine Learning information

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$50.5K

$111.1K

$168K

How much do phd electrical engineering machine learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for phd electrical engineering machine learning in the United States is $111,091.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $132,000.00 per year, depending on experience, location, and employer.

What is a PhD in electrical engineering with a focus on machine learning?

A PhD in Electrical Engineering with a focus on Machine Learning is an advanced research degree that combines core principles of electrical engineering with in-depth study of machine learning algorithms and their applications. Students in this program typically work on developing new methods and technologies that intersect areas such as signal processing, robotics, computer vision, or communications, using machine learning techniques. Graduates are prepared for careers in academia, industrial research, or advanced development roles in technology companies, where they contribute to innovations in automation, intelligent systems, and data-driven engineering solutions.

What types of interdisciplinary collaboration can I expect as a PhD in electrical engineering specializing in machine learning?

As a PhD in Electrical Engineering with a focus on Machine Learning, you will often collaborate with professionals from diverse fields such as computer science, data science, biomedical engineering, and even business or product management. These collaborations might involve working on projects like intelligent sensor systems, autonomous vehicles, or advanced signal processing, where combining expertise is essential for innovation. You'll likely participate in cross-functional team meetings, joint research publications, and interdisciplinary grant proposals, which can broaden your technical skills and expand your professional network.

What are the key skills and qualifications needed to thrive as a PhD electrical engineer specializing in machine learning, and why are they important?

To thrive as a PhD Electrical Engineer specializing in Machine Learning, you need advanced knowledge of signal processing, statistical modeling, and algorithm development, typically supported by a doctoral degree in electrical engineering or a related field. Proficiency with programming languages such as Python or MATLAB, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with hardware integration are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you convey complex technical concepts and collaborate with multidisciplinary teams. These skills ensure the successful design, implementation, and deployment of innovative machine learning solutions to solve challenging engineering problems.

What is the difference between Phd Electrical Engineering Machine Learning vs Data Scientist?

AspectPhd Electrical Engineering Machine LearningData Scientist
Required CredentialsPhD in Electrical Engineering or related, strong machine learning expertiseTypically a master's or PhD in Data Science, Computer Science, or related
Work EnvironmentResearch labs, academia, R&D departments in tech and engineering firmsBusiness, tech companies, consulting firms, often collaborative teams
Industry UsageResearch, development, specialized engineering projects involving ML algorithmsData analysis, predictive modeling, business insights

While both roles involve machine learning, a Phd Electrical Engineering Machine Learning focuses on advanced research and development in engineering contexts, whereas a Data Scientist applies ML techniques to analyze data and generate business insights. The former emphasizes technical depth and research, the latter emphasizes data analysis and communication skills.

What are popular job titles related to Phd Electrical Engineering Machine Learning jobs?

For Phd Electrical Engineering Machine Learning jobs, the most frequently searched job titles are:

Senior Machine Learning Engineer

San Jose, CA • On-site

TetraMem - Accelerate The World
Computer and Peripheral Equipment Manufacturing • 11 - 50 employees
Learn more about TetraMem - Accelerate The Worldexternal

$200K - $280K/yr

Full-time

Re-posted 2 days ago

Key responsibilities

  • Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.

  • Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.

  • Work closely with hardware and software teams to integrate ML models into production systems.

Job description

Responsibilities:

  • Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
  • Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
  • Work closely with hardware and software teams to integrate ML models into production systems.
  • Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
  • Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
  • Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
  • Provide technical leadership and mentorship to junior engineers.
  • Publish research findings, present at conferences, and contribute to open-source projects when applicable.

Requirements:

  • 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
  • Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
  • Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
  • Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
  • Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
  • Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
  • Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
  • Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).

Experience in one or more of the following areas considered a strong plus:

  • Understanding of ML compiler and runtime design.
  • Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
  • Familiarity with hardware acceleration techniques.
  • Experience in embedded system development.

Salary Range: $200,000 - $280,000 / year

TetraMem celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to be an Equal Opportunity Employer and welcome applicants from all backgrounds. Qualified candidates will receive consideration for employment without regard to race, color, religion, creed, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, marital status, medical condition, disability, genetic information, military or veteran status, or any other characteristic protected by applicable federal, state, or local law.
TetraMem is committed to providing reasonable accommodations to qualified applicants with disabilities throughout the recruitment process. Applicants requiring accommodation may contact Human Resources for assistance.
To ensure a fair, consistent, and efficient hiring process, all candidates must apply through TetraMems official ClearCompany Applicant Tracking System (ATS). Applications submitted through the ATS allow our hiring team to evaluate candidates using a standardized process and ensure timely communication throughout the recruitment process. To promote equal consideration for all applicants, applications submitted outside of the ClearCompany ATS, including direct emails, LinkedIn messages, or unsolicited submissions to employees, may not be reviewed or considered.
We encourage all interested candidates to apply through the official TetraMem Careers page.

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