Machine Learning Engineer

About this role

Skills you'll use

What you'll do

• Prototype, train and fine-tune machine-learning and deep-learning models
• Build data pipelines for training, evaluation and inference
• Benchmark models on accuracy, latency and cost, and iterate on the trade-offs
• Work with engineers to deploy, monitor and retrain models in production
• Run error analysis to find where models fail and why
• Research recent papers, tools and techniques and test what is applicable
• Write reusable, well-documented training and evaluation code
• Present experiment results and recommendations to the team

Who can apply

• Bachelor's/Master's degree in B.E./B.Tech/M.Tech/M.Sc in Computer Science, AI, Data Science, Mathematics or a related field
• 0–2 years of professional experience — 2023–2026 graduates and freshers with strong project work are encouraged to apply
• Strong Python and hands-on experience with NumPy, pandas and scikit-learn
• Working knowledge of deep-learning frameworks — PyTorch or TensorFlow
• Understanding of ML fundamentals: loss functions, optimisation, regularisation, evaluation metrics
• Exposure to at least one of NLP, computer vision, LLMs or recommendation systems
• Comfort with Git, Linux basics and working in notebooks and scripts
• Good linear algebra, probability and statistics foundations
• Bonus: Docker, cloud (AWS/GCP/Azure), vector databases or MLOps tooling
• Curiosity, rigour and the patience to debug models that quietly do the wrong thing

Perks & benefits

Interview & selection process

6 rounds — typical selection flow for this role:

1. 1Resume & profile screening
2. 2Online assessment — Python, ML and maths
3. 3Technical interview — ML depth and projects
4. 4Applied/coding round — implement or debug a model
5. 5HR discussion & culture fit
6. 6Offer letter

About Glance

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