Qualcomm Cloud AI platform Staff Engineer 

India, Karnataka, Bengaluru 

906072814

Job Area:

Engineering Group, Engineering Group > Software Engineering

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

• 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

Bachelor’s degree in engineering, Information Systems, Computer Science, or related field and 10+ years of Software Engineering or related work experience OR

Master’s degree in engineering, Information Systems, Computer Science, or related field and 8+ year of Software Engineering or related work experience OR

, Information Systems, Computer Science, or related field.

As a Staff Engineer, your responsibilities will include:

Strong Machine Learning Fundamentals: A deep understanding of machine learning algorithms, statistical modeling, and data preprocessing.

: You'll implement and remodel machine learning models and algorithms, ensuring they align with project objectives.Algorithm Prototypes: Build AI algorithm prototypes based on project specifications.

Performance Assessment: Run tests to evaluate AI performance, analyzing data to identify strengths and weaknesses.

Model Evaluation: Familiarity with metrics like accuracy, precision, recall, and F1-score to assess model effectiveness.

Deployment and Scalability: Knowledge of deploying ML models in production environments and handling scalability challenges.

Algorithm Optimization: Implement changes to algorithms to enhance AI performance.

Programming Languages: Proficiency in languages such as Python, C, or C++ for implementing ML models and data manipulation.

Deep Learning Frameworks: Experience with frameworks like TensorFlow, PyTorch, etc. for neural network development.

Feature Engineering: Ability to create relevant features from raw data to improve model performance.

Domain Expertise: Understanding of the specific industry or domain where ML solutions will be applied.

Troubleshooting and Documentation: Address issues with deployed AI systems, documenting the development process.

Staying Current: Keep up to date with the latest innovations in machine learning and AI in general.

Effective Communication: As a machine learning engineer, you’ll often need to explain complex algorithms and models to various stakeholders. Clear communication to ensure everyone understands the technical details and project progress.

Adaptability: The field evolves rapidly, so staying open to new techniques, tools, and approaches is crucial for a machine learning engineer.

Time Management: Balancing multiple tasks, deadlines, and priorities is essential.

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.