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Machine Learning Electrical Engineering Jobs (NOW HIRING)

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... Electrical Engineering, or related field 5+ years of experience in machine learning, data science, or AI engineering Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch ...

Chabez Tech

Machine Learning Engineer

Portland, OR

... Electrical Engineering, or related field • 5+ years of experience in machine learning, data science, or AI engineering • Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch ...

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... Electrical Engineering, or related field • 5+ years of experience in machine learning, data science, or AI engineering • Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch ...

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... Electrical Engineering, or related field • 5+ years of experience in machine learning, data science, or AI engineering • Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch ...

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

As of Jun 3, 2026, the average yearly pay for machine learning electrical engineering 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 are the key skills and qualifications needed to thrive as a Machine Learning Electrical Engineer, and why are they important?

To thrive as a Machine Learning Electrical Engineer, you need a strong background in electrical engineering principles, mathematics, and proficiency in programming languages such as Python or MATLAB, often supported by a relevant degree. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch), embedded systems, and data analysis tools is typically required, along with certifications in AI or data science being advantageous. Analytical thinking, creativity, and effective communication are essential soft skills for developing innovative solutions and collaborating across multidisciplinary teams. These competencies are crucial for designing intelligent systems that bridge hardware and software, driving advancements in smart technologies.

How do machine learning engineers and electrical engineers typically collaborate on projects involving smart hardware devices?

In projects involving smart hardware devices, machine learning engineers and electrical engineers often work closely from the initial design phase through deployment. Electrical engineers focus on designing and optimizing the hardware—such as sensors, circuits, or embedded systems—while machine learning engineers develop algorithms that process data collected by these devices. Collaboration is crucial for ensuring that the hardware can support the computational requirements of the models, and vice versa. Regular meetings and cross-functional teams are common, allowing both sides to address challenges like data quality, power consumption, and real-time processing. This teamwork not only ensures successful product development but also provides ample learning opportunities for professionals in both fields.

What is machine learning in electrical engineering?

Machine learning in electrical engineering involves applying algorithms and statistical models to analyze and interpret data from electrical systems. This can include tasks like fault detection, power grid optimization, signal processing, and automation of control systems. Electrical engineers use machine learning to improve system reliability, efficiency, and to develop smart devices. The integration of machine learning enhances traditional engineering methods by enabling predictive maintenance, adaptive controls, and intelligent decision-making.

Can electrical engineers work in machine learning?

Electrical engineers can work in machine learning by applying their knowledge of signal processing, systems, and hardware to develop algorithms, sensors, and embedded systems. Many roles require programming skills in languages like Python or MATLAB and understanding of data analysis and neural networks. Transitioning often involves gaining expertise in machine learning frameworks and data science concepts.

What is the difference between Machine Learning Electrical Engineering vs Electrical Engineering?

AspectMachine Learning Electrical EngineeringElectrical Engineering
Required CredentialsBachelor's or Master's in Electrical Engineering, plus knowledge of machine learningBachelor's or Master's in Electrical Engineering, focus on circuits, systems, and power
Work EnvironmentResearch labs, tech companies, AI-focused projectsPower plants, manufacturing, infrastructure, and electronics industries
Industry UsageAI integration in electrical systems, smart devices, automationPower systems, electronics, telecommunications, control systems

Machine Learning Electrical Engineering combines electrical engineering principles with machine learning techniques to develop intelligent systems. In contrast, Electrical Engineering focuses on designing and maintaining electrical systems and infrastructure. While both roles require a strong foundation in electrical concepts, Machine Learning Electrical Engineering emphasizes AI and data-driven solutions, often within tech and research environments, whereas Electrical Engineering covers a broader range of electrical systems across various industries.

Machine Learning Engineer

Chabez Tech

Portland, OR • On-site

Contractor

Posted 9 days ago

Job description

Job Description

Job Title: Machine Learning Engineer
Location: Portland, OR - Onsite (Local only / F2F interview)
Duration: 24 Months Contract

Experience Level: 5+ years of experience

Required Qualifications
    Bachelor's or master's degree in computer science, Machine Learning, Electrical Engineering, or related field 
    5+ years of experience in machine learning, data science, or AI engineering 
    Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) 
    Experience with time-series data analysis and anomaly detection 
    Hands-on experience with causal inference methods (e.g., Bayesian networks, structural causal models) 
    Experience building or working with knowledge graphs (Neo4j, RDF, graph databases) 
    Understanding of explainable AI techniques (SHAP, LIME, counterfactual analysis) 
    Experience deploying ML models in production systems 
    Strong problem-solving skills and ability to work with complex, real-world datasets

Preferred Qualifications
    Experience with fault tree analysis (FTA), reliability engineering, or failure analysis 
    Background in industrial systems, semiconductors, manufacturing, or IoT environments 
    Experience with graph-based ML / Graph Neural Networks (GNNs) 
    Familiarity with RCA methodologies (FMEA, 5 Whys, fishbone diagrams) 
    Experience with vector databases, RAG systems, or LLM-based reasoning 
    Knowledge of MLOps practices (CI/CD, monitoring, model governance) 
    Experience working in air-gapped or high-security environments 
 

Additional Information

All your information will be kept confidential according to EEO guidelines.

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