Are you passionate about cutting-edge AI and machine learning with aspirations to work on real-world edge computing solutions? The Software Engineering Intern – ML position at Blaize Hyderabad offers an exceptional 6-month opportunity to work on advanced AI platforms powering critical infrastructure across smart cities, defense, healthcare, and automotive sectors.
Blaize is building a hybrid AI platform engineered for edge-to-cloud intelligence at scale. With over 200 employees worldwide and headquarters in California, Blaize delivers efficient, scalable AI for complex multimodal workloads. This internship provides hands-on experience with deep learning model development, optimization, deployment, and maintenance while working on their full-stack programmable processor architecture and low-code/no-code software platform.
🚀 About the ML Engineering Internship at Blaize
This 6-month internship focuses on developing and implementing deep learning models for real-world business problems. You’ll work extensively with model training, fine-tuning, dataset curation, and performance optimization. The role involves working with transformers, large language models (xLLM), and cutting-edge AI frameworks while contributing to solutions deployed at the network’s edge and in data centers for high-performance computing applications.
📊 Job Details
| Detail | Information |
|---|---|
| Company | Blaize |
| Position | Software Engineering Intern – ML |
| Location | Hyderabad, India |
| Job Type | Full-time Internship |
| Duration | 6 Months |
| Industry | Edge AI / Hardware-Software Co-design |
| Global Presence | USA, UK, UAE, India |
Primary Job Responsibilities
Core Development Functions
- Develop and implement deep learning models to solve specific business problems across various industry verticals
- Select appropriate algorithms based on problem requirements and performance constraints
- Train, fine-tune, and optimize models for accuracy and computational efficiency
- Work extensively with dataset curation ensuring high-quality training data
- Perform comprehensive data preprocessing and feature engineering for model improvement
- Optimize model performance balancing accuracy with efficiency for edge deployment
- Monitor deployed models continuously for issues such as model drift and performance degradation
- Debug problems systematically using structured troubleshooting approaches
- Retrain models as needed to maintain performance standards in production
- Conduct comprehensive testing and validation of developed solutions
- Troubleshoot edge cases and ensure reliability before production deployment
- Implement research papers and translate academic concepts into practical solutions
- Work with knowledge distillation techniques to create efficient model variants
- Collaborate with cross-functional teams including hardware engineers and software developers
- Document model architectures, training procedures, and deployment workflows
- Stay updated with latest developments in transformers and large language models
Help a friend land their next role. Share now!
📋 Required Qualifications and Skills
Educational Requirements
- MS/M.Tech/BS/B.Tech in Computer Science, Electronics and Communication Engineering (ECE), or Machine Learning/Artificial Intelligence
- Strong academic foundation in mathematics and programming
Core Technical Skills
| Skill Category | Requirements |
|---|---|
| Programming | Proficiency in Python, C++, Assembly |
| ML Frameworks | PyTorch, ONNX (proficient level) |
| AI/ML Expertise | Deep Learning, Machine Learning, Transformers, xLLM |
| Model Lifecycle | Training, Fine-tuning, Evaluation, Optimization |
| Data Engineering | Data Curation, Preprocessing, Feature Engineering |
| Deployment | Model Deployment and production experience |
| Mathematics | Strong understanding of Linear Algebra fundamentals |
Additional Desired Skills
- Ability to read, understand, and implement academic research papers
- Familiarity with Knowledge Distillation techniques for model compression
- Participation in Kaggle competitions demonstrating practical ML skills
- Demonstrated personal ML projects showcasing initiative and learning
- Experience with edge computing or hardware-aware model optimization
- Understanding of model quantization and pruning techniques
- Familiarity with MLOps practices and tools
Essential Competencies
- Strong analytical and problem-solving abilities
- Systematic debugging and troubleshooting skills
- Attention to detail in model evaluation and testing
- Ability to work independently and in collaborative teams
- Good communication skills for technical documentation
- Passion for staying current with AI/ML research
- Results-oriented mindset with focus on production deployment
Follow us on 
LinkedIn for the latest updates
Follow us on 
Threads for the latest updates
Subscribe ▶️ YouTube Channel for Latest Updates
💰 Expected Stipend Range
| Duration | Monthly Stipend Range |
|---|---|
| 6 Months | ₹40,000 – 60,000 per month |
Note: Blaize offers competitive compensation for ML internships aligned with industry standards for edge AI companies. Actual stipend depends on educational background (MS/BS), technical skills proficiency, research experience, and interview performance. Potential for full-time conversion based on outstanding performance.
How to Prepare for the Application
Technical Preparation
- Master PyTorch and build end-to-end deep learning projects
- Implement transformer models and fine-tune pre-trained models
- Participate in Kaggle competitions focusing on model optimization
- Study edge computing constraints and model efficiency techniques
Research and Implementation
- Read recent papers on transformers, LLMs, and knowledge distillation
- Implement 2-3 research papers from scratch to demonstrate understanding
- Create projects showing model deployment and monitoring capabilities
- Practice feature engineering and data preprocessing workflows
Portfolio Development
- Build GitHub repository with well-documented ML projects
- Include projects demonstrating model training, optimization, and deployment
- Showcase understanding of model efficiency and edge constraints
- Create technical blogs explaining your implementations and learnings
About Blaize Technology
Company Overview
Blaize specializes in edge AI computing with focus on:
- Full-stack programmable processor architecture
- Low-code/no-code software platform for AI development
- Real-time AI processing for edge and data center
- High-performance computing with low power consumption
- Scalable solutions for complex multimodal workloads
Application Tips
- Highlight projects involving PyTorch, transformers, and model optimization
- Showcase Kaggle competition participation with rankings and learnings
- Emphasize experience with model deployment and production ML
- Demonstrate ability to implement research papers with concrete examples
- Express interest in edge computing and hardware-aware AI optimization
- Include projects showing data preprocessing and feature engineering skills
- Prepare to discuss trade-offs between model accuracy and efficiency
- Research Blaize’s technology platform and industry applications
- Show understanding of challenges in deploying AI at the edge
- Prepare questions about hardware-software co-design and optimization
Disclaimer: This job information is collected from official and publicly available sources. We do not charge any fees for job applications, do not guarantee recruitment, and are not responsible for any loss or damage arising from reliance on this information.
Share the opportunity
My name is Ashok. I am from Andhra Pradesh and currently staying in Hyderabad. I completed my B.Tech in 2022 from Annamacharya Institute of Technology and Sciences with an aggregate of 83%. After graduation, I trained in Core Java and worked on mini projects like a Flipkart clone and interactive dashboards using Power BI. Later, I pursued an AI course and developed projects such as a movie recommendation system and a sentiment analysis tool using Python, Scikit-learn, and NLP techniques. I am passionate about learning new technologies and look forward to contributing my skills while growing with an innovative team.