Sr Engineer/ Lead Sr Enginner, Data Analytics and ML Model Development

Qualcomm; Bengaluru; On-site; INR 900,000 - 1,200,000; Full time

Job summary

Qualcomm is seeking a Hardware Engineer to plan, design, and test cutting-edge electronic systems in Bengaluru. Candidates will need to design data analytics workflows and develop machine learning models that meet semiconductor engineering needs.

The ideal applicant will have strong programming skills, experience with data analysis, and the ability to collaborate with cross-functional teams. Qualified candidates should have a degree in a related field and several years of relevant experience.

Qualifications

• 3+ years of Hardware Engineering experience with a Bachelor's degree.
• 2+ years of experience with a Master's degree.
• 1+ year of experience with a Ph.D.

Responsibilities

• Design and implement data analytics workflows for semiconductor use cases.
• Develop and optimize ML models using various techniques.
• Build scalable data transformation pipelines.
• Conduct statistical analysis and time-series analysis.

Skills

Programming in Python

Data analysis using Pandas

Experience with machine learning models

Analytical skills

Proficiency in C++

Education

Bachelor’s degree in Computer Science or related field

Master’s degree in relevant field

Ph.D. in related field

Tools

TensorFlow

PyTorch

SQL databases

Job description

Company: Qualcomm India Private Limited

Engineering Group: Hardware 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. As a Qualcomm Hardware Engineer, you will plan, design, optimize, verify, and test electronic systems, bring‑up yield, circuits, mechanical systems, digital/analog/RF/optical systems, equipment and packaging, test systems, FPGA, and/or DSP systems for cutting‑edge products. You’ll collaborate with cross‑functional teams to develop solutions that meet performance requirements.

Key Responsibilities

• Design and implement end‑to‑end data analytics workflows tailored to semiconductor engineering use cases.
• Develop, train, and optimize ML models using supervised, unsupervised, and deep learning techniques.
• Perform feature engineering, model evaluation, and hyperparameter tuning to improve model performance.
• Build scalable data ingestion and transformation pipelines using Python and cloud‑native tools.
• Apply statistical analysis and visualization techniques to derive insights from large datasets.
• Conduct time‑series analysis using methods such as ANOVA and other statistical techniques.
• Experiment with GenAI platforms, LLMs, and RAG techniques to enhance model capabilities.
• Collaborate with cross‑functional teams to integrate ML models into engineering workflows and tools.
• Document methodologies and provide technical guidance to internal teams.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 3+ years of Hardware Engineering experience.
• Master’s degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 2+ years of Hardware Engineering experience.
• Ph.D. in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 1+ year of Hardware Engineering experience.
• Strong programming skills in Python or C++.
• Solid understanding of data structures, algorithms, and software design principles.
• Strong analytical and problem‑solving skills.
• Hands‑on experience with supervised and unsupervised learning techniques, including classification, clustering, and dimensionality reduction.
• Experience with deep neural network architectures such as RNNs and Transformers.
• Experience with ML frameworks such as scikit‑learn, TensorFlow, or PyTorch.
• Proficiency in data analysis using Pandas, NumPy, and Matplotlib.

Preferred Qualifications

• Ph.D. in Computer Science, Data Science, or related field.
• Experience in large model development & training from scratch.
• Familiarity with time‑series analysis methods including ANOVA, ARIMA, and seasonal decomposition.
• Familiarity with SQL databases, ETL frameworks, and cloud platforms (e.g., AWS).
• Knowledge of LLM integration frameworks (e.g., LangChain).
• Hands‑on experience with distributed computing and cloud‑scale data processing.
• Software design & architecture experience.

Qualcomm is an equal opportunity employer.