Qualcomm is hiring Fresher’s and Experienced candidates for Machine Learning Engineer Role. The complete details about Drive are as follows.
Job Details :
Qualcomm Recruitment For Machine Learning Engineer
Company : Qualcomm
Job Role : Machine Learning Engineer
Degree : Any Bachelor’s degree
Batch : 2024 – 2019
Experience : 0 – 2 Year’s
Location : Hyderabad, India
Qualcomm – Required Qualifications & Skills :
- Bachelor’s degree in Engineering, Information Systems, Computer Science, or related field.
- Educational Background: A degree in Computer Science, Electrical Engineering, Mathematics, or a related field. Advanced degrees (Master’s or Ph.D.) are often preferred.
- Experience: Experience in machine learning, deep learning, and statistical modeling. Familiarity with frameworks such as TensorFlow, PyTorch, or scikit-learn is common.
- Programming Skills: Proficiency in languages such as Python, C++, or Java. Knowledge of software engineering principles and practices is beneficial.
- Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to handle complex data and derive actionable insights.
Skills :
- Machine Learning Frameworks: Experience with frameworks and tools like TensorFlow, Keras, PyTorch, and scikit-learn.
- Data Manipulation: Skills in data manipulation libraries (e.g., Pandas, NumPy).
- Mathematics: A solid understanding of linear algebra, calculus, probability, and statistics.
- Software Development: Familiarity with version control systems (e.g., Git), software development methodologies, and best practices.
Roles and Responsibilities :
- Model Development: Designing, developing, and optimizing machine learning models for various applications such as computer vision, natural language processing, and predictive analytics.
- Data Handling: Working with large datasets, preprocessing, feature engineering, and ensuring data quality.
- Algorithm Implementation: Implementing and optimizing algorithms on different platforms, including embedded systems and mobile devices.
- Collaboration: Working with cross-functional teams, including software engineers, hardware engineers, and product managers, to integrate machine learning solutions into products.
Application Process
- Resume Submission: Apply through Qualcomm’s careers page or job boards with a tailored resume highlighting relevant experience.
- Screening: Initial screening might involve a review of your resume and a potential phone interview with HR.
- Technical Interview: Expect technical interviews focusing on machine learning concepts, algorithms, coding skills, and problem-solving abilities. There may also be a practical component or coding challenge.
- Behavioral Interview: Interviews with team members and managers to assess cultural fit, teamwork, and communication skills.
- Offer and Negotiation: If successful, you’ll receive an offer. Be prepared to discuss compensation, relocation (if applicable), and other terms.
Preparing for the Interview
- Review Fundamentals: Brush up on core machine learning concepts, algorithms, and recent advancements in the field.
- Practice Coding: Solve problems on platforms like LeetCode or HackerRank to sharpen your programming skills.
- Prepare for System Design: Be ready to discuss how you would approach designing scalable and efficient machine learning systems.
- Research Qualcomm: Understand their products, technologies, and recent developments to tailor your responses and questions.
Apply To All The Jobs ( Fresher’s & Expereinced ) : Click Here
Interested candidates can apply to the drive before link expires.
Apply link : Click Here
Note:– Only shortlisted candidates will receive the call letter for further rounds.
Here are 10 machine learning engineer interview questions
- Explain the Bias-Variance Tradeoff.
- How Does a Convolutional Neural Network (CNN) Work?
- What is Regularization, and How Does It Help Prevent Overfitting?
- Describe How You Would Handle Missing Data in a Dataset.
- How Do You Evaluate the Performance of a Regression Model?
- What Are Some Common Feature Engineering Techniques?
- How Does Gradient Descent Work, and What Are Its Variants?
- What Are Precision and Recall, and How Are They Different from Each Other?
- Describe a Time When You Had to Improve the Performance of a Model. What Steps Did You Take?
- How Would You Approach Designing a Machine Learning System for a New Product or Feature?