Are you a student or recent graduate looking to build a career in Artificial Intelligence and Machine Learning? Daice Labs is offering a paid AI/ML Engineering Internship for candidates based in India, with a remote work arrangement and potential for extension.
This opportunity is designed for aspiring AI engineers who want to move beyond theoretical learning and contribute to real machine learning projects. Interns will work alongside experienced engineers and researchers, supporting model development, experimentation, data pipelines, code reviews, and technical documentation.
🏢 About Daice Labs
Daice Labs is an AI technology company founded by scientists associated with MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The company is developing hybrid AI infrastructure for handling complex, long-running tasks.
Its approach combines advanced AI models with neurosymbolic methods and bio-inspired system design. The goal is to support more adaptable and verifiable AI systems with persistent context, governance, and domain-specific capabilities.
📋 Job Overview
- 🏢 Company: Daice Labs
- 💼 Job Role: AI/ML Engineering Intern
- 📍 Location: India
- 🌐 Work Mode: Remote
- 🎓 Eligible Candidates: Students or recent graduates
- 🗓️ Internship Duration: Initially 3 months, with potential extension; the detailed description also mentions 3–6 months depending on project needs
- 💰 Compensation: Paid internship; details discussed during the interview process
- 💻 Programming Language: Python
- 🧠 ML Frameworks: PyTorch or TensorFlow
🛠️ Key Responsibilities
- 🤖 Model Development: Assist with designing, implementing, and improving machine learning models.
- 🧪 Experimentation: Run experiments, evaluate model performance, compare results, and document findings.
- 📊 Data Pipeline Development: Help build and maintain data preprocessing workflows and dataset management processes.
- 🧹 Data Preparation: Clean, transform, validate, and organize datasets for training and evaluation.
- 💻 Codebase Improvements: Contribute code enhancements, testing, debugging, and technical documentation.
- 🔍 Code Reviews: Participate in reviews and learn engineering best practices from experienced developers.
🌟 Why Is This Role Good for Students and Fresh Graduates?
- 🚀 Real-World ML Exposure: Apply machine learning concepts to active projects rather than only academic exercises.
- 🧑🏫 Technical Mentorship: Work alongside senior engineers and researchers and learn from their feedback.
- 🧪 Research and Experimentation: Develop skills in designing experiments, evaluating results, and drawing evidence-based conclusions.
- 📊 Data Engineering Fundamentals: Understand how data quality, preprocessing, and reproducible pipelines affect model performance.
- 🧠 Emerging AI Technologies: Gain potential exposure to hybrid AI architectures, generative models, and symbolic AI approaches.
- 🌐 Remote Work Experience: Strengthen independent problem-solving, written communication, and time management.
🧠 Skills Required
- 🐍 Python Programming: Functions, classes, data structures, exception handling, and writing readable code.
- 🧠 Machine Learning Fundamentals: Supervised and unsupervised learning, training and validation, overfitting, and evaluation metrics.
- 🔥 PyTorch or TensorFlow: Basic model implementation, tensor operations, training loops, and model evaluation.
- 📊 Statistics and Mathematics: Probability, distributions, basic linear algebra, and statistical reasoning.
- 🧹 Data Preprocessing: Handling missing values, encoding categorical data, normalization, and dataset splitting.
- 🔧 Git and Version Control: Commits, branches, pull requests, and collaborative development workflows.
- 🔍 Analytical Thinking: Investigating unexpected results, comparing experiments, and communicating conclusions.
- 🤝 Communication: Writing clear documentation and collaborating effectively with a remote team.
🎤 Expected Interview Rounds
- 📞 Round 1 – Application or Introductory Screening: Discuss your education, interest in AI/ML, availability, and relevant projects.
- 🐍 Round 2 – Python and ML Fundamentals: Expect questions on data structures, basic algorithms, model evaluation, overfitting, and data preprocessing.
- 🧪 Round 3 – Practical Technical Assessment: You may be asked to implement a simple model, prepare a dataset, evaluate results, or debug a Python notebook.
- 🧠 Round 4 – Project or Research Discussion: Explain a project you built, your approach to experimentation, technical challenges, and lessons learned.
- 🤝 Round 5 – Team or Researcher Discussion: Discuss problem-solving, code quality, remote collaboration, and your interest in hybrid AI systems.
💰 Expected Salary Range
The company has confirmed that this is a paid internship but has not disclosed a stipend amount. For planning purposes, candidates may use a broad, non-company-specific estimate of ₹10,000–₹50,000 per month for paid AI/ML internships in India. This is only an indicative benchmark, not a verified Daice Labs compensation range. The actual stipend may differ based on responsibilities, skills, and the company’s compensation structure.
📚 Preparation Tips
- 🐍 Strengthen Python: Practise lists, dictionaries, functions, classes, NumPy, and pandas.
- 🧪 Build a complete ML project: Select a dataset, preprocess it, train a baseline model, evaluate performance, and document the results.
- 🔥 Practise one ML framework: Be comfortable explaining a simple training loop and model evaluation in PyTorch or TensorFlow.
- 📊 Understand experimentation: Learn train/validation/test splits, baseline comparisons, reproducibility, and avoiding data leakage.
- 🧠 Explore hybrid AI concepts: Understand the basic difference between neural approaches and symbolic reasoning, without assuming advanced expertise is mandatory.
- 🔧 Use GitHub effectively: Maintain clean project repositories with a README, setup instructions, experiment results, and understandable code.
📝 Resume Tips for AI/ML Candidates
- 🎯 Highlight relevant projects: Place your strongest machine learning or data science projects near the top of your resume.
- 📊 Explain your methodology: Describe the dataset, preprocessing, model selection, evaluation metrics, and results.
- 💻 List tools accurately: Include Python, PyTorch or TensorFlow, pandas, NumPy, scikit-learn, and Git only where applicable.
- 🧪 Show experimentation: Mention model comparisons, performance improvements, or debugging achievements supported by actual results.
- 🔗 Include project repositories: If available, provide working project or portfolio links in your resume; keep the application email itself concise.
- ✅ Demonstrate ownership: Explain your individual contribution clearly, especially for academic or team projects.
📩 How to Apply
👉 Application Link: Click Here
Interested students and recent graduates should apply through the recruitment channel where the Daice Labs vacancy was originally published. Prepare an updated resume highlighting Python, ML fundamentals, relevant frameworks, and project experience. The supplied job description does not include a direct recruiter email or a confirmed application address, so use the original vacancy’s application instructions rather than an unverified contact.
🌟 Final Thoughts
The Daice Labs AI/ML Engineering Internship could be a valuable opportunity for aspiring AI engineers seeking hands-on exposure to model development, experimentation, and emerging AI infrastructure. Focus on demonstrating your Python fundamentals, explaining a complete ML project, and showing that you can learn independently in a remote environment.
🚀 Build, experiment, document, and keep learning—your AI/ML career starts with practical experience!
