ZipRecruiter

Log In

1

Machine Learning Electrical Engineering Jobs (NOW HIRING)

SynergisticIT

Machine Learning Engineer

Nashville, TN · On-site

Machine Learning And Artificial Intelligence Developer You will be responsible for Machine Learning ... Computer Engineering/ Information Systems/Information Technology/ Electrical Engineering ...

Motorola

Machine Learning Engineer

Los Angeles, CA

Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 2 years of experience in machine learning, with demonstrated ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

Required : • Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or ... Machine Learning, including ownership of projects throughout the entire ML Lifecycle • ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

As part of our machine learning team, you will play a vital role in prototyping foundational ... MS/PhD in computer vision, electrical, optical or computer engineering or related fields.Experience ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field * Demonstrated expertise in model development, optimization, and algorithmic ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA · On-site

S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field * Demonstrated expertise in model development, optimization, and algorithmic ...

Qualcomm

Modem Machine Learning Engineer

San Diego, CA · On-site

The Modem Machine Learning Engineer applies advanced machine learning techniques to next-generation ... OR Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

Bachelors degree in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics ... Strong foundation in machine learning, statistics, signal processing, or applied mathematics for ...

Navstar

Machine Learning Engineer

Centreville, VA · On-site

$102K - $144K/yr

Machine Learning Engineer II The Machine Learning Engineer II will be a member of the Learning and ... Mathematics, Optimization, Computer Science/Engineering, Electrical Engineering, Aerospace, or ...

next page

Showing results 1-20

People also search for

All JobsMachine Learning Electrical Engineering Jobs

Machine Learning Electrical Engineering information

See salary details

$50.5K

$111.1K

$168K

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

As of Jun 4, 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.

Software Engineer - Machine Learning

Software Engineer - Machine Learning

FocusKPI Inc.

Mountain View, CA • On-site

$95 - $110/hr

Contractor

Posted 23 days ago

Job description

FocusKPI is seeking a Software Engineer - Machine Learning to join one of our clients, a high-tech SaaS company.
We are looking for an experienced Machine Learning Engineer to lead the development of prompt injection and prompt safety models to protect the client's downstream agentic AI systems across phones, the cloud, and XR/AR. You will design, train, and deploy classifier and guardrail models (both cloud-based and hybrid on-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post-training these models with RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions provide.
Work Location: Mountain View, CA (Onsite role, 5 days/week onsite)
Duration: 12-month contract with potential to extend the contract depending on your performance & budget
Pay Range: $95 - 110/hr
**No C2C resumes are considered**
Position Responsibilities:
  • Design and train prompt-injection detection models and prompt-safety classifiers that operate on both inputs to and outputs from the client's agentic AI systems.
  • Build hybrid deployment pipelines that split safety inference between on-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
  • Apply post-training techniques (e.g. RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
  • Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool-use exploits, and unsafe-output cases drawn from red-teaming and production signals.
  • Build evaluation harnesses that measure attack success rate, false-positive rate, latency, and on-device footprint across model iterations and threat categories.
  • Partner with agent, device, and platform teams to integrate safety models into mobile-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
  • Work cross-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.
Qualifications:
  • M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
  • 3+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems.
  • 2+ years of industry experience in software engineering.
  • Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
  • Hands-on experience post-training LLMs with RLHF, DPO, RLAIF, or reward modeling, including reward design, preference data curation, and training stability.
  • Hands-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
  • Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).
Preferred Qualifications:
  • Experience building safety or moderation systems for agentic AI: tool-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
  • Experience with red-teaming, adversarial data generation, or automated attack pipelines (e.g., GCG, PAIR, generator-critic frameworks).
  • Experience with on-device or edge ML deployment (ExecuTorch, Core ML, TFLite, MLC-LLM, vendor NPU toolchains) and model compression (quantization, distillation, pruning) for safety models.
  • Experience with telemetry, logging, or user-facing data systems on mobile, XR/AR, or consumer platforms, including privacy-preserving handling of user data (e.g., anonymization, on-device processing, federated approaches).
  • Publications at top-tier ML/NLP/security venues (NeurIPS, ICML, ICLR, ACL, EMNLP, USENIX Security, IEEE S&P), patents, or open-source contributions in the safety, alignment, or AI security space.

**No C2C resumes are considered**
Thank you!
FocusKPI Hiring Team
Founded in 2010, FocusKPI, Inc. (FocusKPI) is a data science and technology firm specializing in predictive analytics practice and methodologies. FocusKPI is a US company headquartered in Silicon Valley, California, with an East Coast office in Boston, Massachusetts.
NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.
FocusKPI logo

About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

View All FocusKPI Jobs

Apply