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Phd Electrical Engineering Machine Learning Jobs

Daman Inc

Machine Learning Engineer

San Antonio, TX · On-site

The Machine Learning (ML) Engineer, with an interest in a variety of disciplines, will strive to ... Bachelor's Degree or foreign equivalent in Computer Science, Electrical Engineering, Mathematics ...

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Apple

Machine Learning Engineer - Health AIML

Cupertino, CA · On-site

$216K - $324K/yr

Description In this role, you will use your skills and experience in software engineering, machine learning, deep learning, and generative AI to design, implement, tune, and evaluate machine learning ...

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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 Data Scientist / Machine Learning Engineer

Tysons, VA

Waypoint Human Capital
See more jobs at Waypoint Human Capitalexternal

Full-time

Re-posted 12 days ago

Job description

Position Title: Senior Data Scientist / Machine Learning Engineer
Position Type: Full-Time, On-Site
Position Location: Tysons, VA
Clearance Required: Active TS/SCI with CI Polygraph or Full Scope Polygraph
Waypoint's client is seeking a dynamic Senior Data Scientist / Machine Learning Engineer with an active TS/SCI CI Poly or higher to join their team. The Senior Data Scientist / Machine Learning Engineer will work directly with data scientists, software engineers, and subject matter experts in the definition of new analytics capabilities able to provide federal customers with the information they need to make proper decisions and enable their digital transformation.
This position works directly with data scientists, software engineers, and subject matter experts to research, design, and deploy machine learning algorithms that support federal customers in digital transformation and data-driven decision making. They will contribute to new analytics capabilities and assist customers in building their own applications. This position requires a bachelor's degree in Computer Science, Electrical Engineering, Statistics, or a related field, 5 to 10 years of relevant experience, and strong Python and applied ML skills. An active TS/SCI with CI Polygraph or Full Scope Polygraph is required.
Responsibilities
The responsibilities include, but are not limited to:
  • Research, design, implement, and deploy Machine Learning algorithms for enterprise applications.
  • Assist and enable federal customers to build their own applications.
  • Contribute to the design and implementation of new features.

Required
  • Active Top Secret clearance with CI Polygraph or Full Scope Polygraph.
  • Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or equivalent fields required.
  • MS or PhD in Computer Science, Electrical Engineering, Statistics, or equivalent fields preferred.
  • Minimum 5–10 years relevant work experience preferred.
  • Excellent programming skills in Python.
  • Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
  • Strong mathematical background (linear algebra, calculus, probability, and statistics).
  • Experience with scalable Machine Learning (MapReduce, streaming).
  • Ability to drive a project and work both independently and in a team.
  • Smart, motivated, can-do attitude, and seeks to make a difference.
  • Excellent verbal and written communication skills.
  • Passion for developing team-oriented solutions to complex engineering problems.
  • Thrive in an autonomous, empowering, and exciting environment.
  • Ability to collaborate across multiple functional teams to improve scalability.
  • Ability to convey highly technical concepts and information in written form to both technical and non-technical audiences.
  • Ability to work on multiple concurrent projects.
  • Strong self-motivation and the ability to work with minimal supervision.
  • Team-oriented, energetic, results- and delivery-focused, with a strong commitment to quality and meeting deadlines.
  • Ability to work in an Agile environment.

Desired
  • Hands-on experience deploying and operating applications using IaaS and PaaS on major cloud providers, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
  • Proficient in leveraging modern LLM tools to accelerate development workflows and enhance code quality.
  • Experience with deep learning.
  • Experience with natural language processing (NLP).
  • Experience with computer vision.
  • Experience with reinforcement learning.