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Trainee Edge Ai Machine Learning Jobs (NOW HIRING)

BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...

Bee Genius is building the future of work and is seeking an AI/Machine Learning Engineer to join their team. The role involves developing and implementing machine learning models and algorithms to ...

As an AI & Machine Learning Engineer, you will design, build, and deploy the intelligent systems ... Optimize model inference for edge deployment on GPU‑accelerated hardware in production Computer ...

## Werkstudent AI/Machine Learning (m/w/d)Applylocations: Leutkirch Werk 1time type: Part timeposted on: Posted 4 Days Agojob requisition id: JR101468Als familiengeführtes Stiftungsunternehmen mit ...

The Samsara AI team builds end-to-end AI solutions for our customers as well as core ML ... Machine Learning Engineer. * Profound experience in optimizing ML models and systems for Edge ...

$120 - $150/hr

The AI/Machine Learning Engineer IIwill be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

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Trainee Edge Ai Machine Learning information

What is the difference between Trainee Edge Ai Machine Learning vs Data Analyst?

AspectTrainee Edge Ai Machine LearningData Analyst
Required CredentialsBasic programming, introductory AI/ML coursesStatistics, data visualization, Excel, SQL
Work EnvironmentTech companies, AI startups, R&D labsBusiness, finance, marketing departments
Industry UsageDeveloping AI models, machine learning pipelinesInterpreting data, generating reports

While both roles involve working with data, Trainee Edge Ai Machine Learning focuses on developing and training AI models, requiring programming and machine learning knowledge. Data Analysts primarily interpret existing data to inform business decisions, emphasizing statistical analysis and visualization skills. The roles differ in technical depth and focus but often overlap in data handling and industry usage.

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Infographic showing various Trainee Edge Ai Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI/Machine Learning Engineer

4 Staffing Corp

Fremont, CA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

AI/Machine Learning Engineer

Fremont, CA, United States

About the Job

Our client is seeking a highly skilled and motivated AI/Machine Learning Engineer to join their team. As an AI/Machine Learning Engineer, you will play a crucial role in developing and implementing cutting-edge machine learning algorithms and AI models to solve complex problems and drive innovation. You will work closely with a cross-functional team of data scientists, software engineers, and domain experts to design, train, evaluate, and deploy machine learning models in production environments.

Responsibilities

  • Design and develop machine learning models and algorithms for various applications, such as natural language processing, computer vision, predictive analytics, and recommendation systems.
  • Collaborate with data scientists, software engineers, and domain experts to understand project requirements, identify data sources, and define appropriate machine learning approaches.
  • Preprocess and clean large datasets to extract relevant features and optimize model performance.
  • Implement and fine-tune machine learning models using popular frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.
  • Conduct experiments to evaluate model performance, analyze results, and iterate on model designs to achieve optimal accuracy, efficiency, and scalability.
  • Collaborate with software engineering teams to integrate machine learning models into production systems and deploy them at scale.
  • Monitor and maintain deployed models, ensuring their performance and reliability over time.
  • Stay up to date with the latest advancements in machine learning and AI technologies, and proactively propose innovative solutions and improvements.

Requirements

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus.
  • Strong background in machine learning, artificial intelligence, and statistical analysis.
  • Demonstrated experience in designing, developing, and deploying machine learning models and algorithms.
  • Proficiency in programming languages such as Python, R, or Java, with a solid understanding of data structures, algorithms, and software engineering principles.
  • Hands-on experience with popular machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.
  • Familiarity with deep learning techniques and frameworks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
  • Experience with data preprocessing, feature engineering, and model evaluation techniques.
  • Strong analytical and problem-solving skills, with the ability to think critically and creatively to tackle complex challenges.
  • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
  • Ability to adapt quickly to new technologies and methodologies in the rapidly evolving field of AI and machine learning.

Preferred Qualifications

  • Experience with cloud platforms (e.g., AWS, Azure, or GCP) and distributed computing frameworks (e.g., Apache Spark) for training and deploying machine learning models at scale.
  • Knowledge of big data technologies, such as Hadoop and Spark, for processing and analyzing large datasets.
  • Experience with computer vision, natural language processing, or other specialized domains within AI/ML.
  • Publications or contributions to the AI/ML community, such as research papers or open-source projects.

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