Machine Learning Intern

Location: Bengaluru

Mode & Duration: Full-time Internship (3–6 months) |

About LANE

LANE is building an AI-based driving intelligence platform that detects and scores real-world driving behavior from mobile sensor data. Were a small, deep-tech team solving hard signal-processing and pattern-recognition problems with real vehicle data — not toy datasets.

What Youll Do

• Analyze time-series sensor data from real driving sessions to identify behavioral patterns and events
• Design, train, and validate ML models for event classification and severity scoring
• Own the full model lifecycle — data prep, feature engineering, training, evaluation, and iteration
• Drive empirical threshold calibration on real, noisy session data over theoretical assumptions
• Debug and improve detection accuracy against ground-truth annotated data

Must-Have Skills

• Strong ML fundamentals — you deeply understand how models actually learn: loss functions, gradient descent, bias-variance, regularization, overfitting, train/val/test discipline, evaluation metrics
• Hands-on model-building experience — youve personally implemented and trained models on real projects (not just run notebooks end-to-end). Be ready to walk us through one in depth
• Strong mathematical fundamentals — statistics, probability, linear algebra, hypothesis testing
• Python for ML — NumPy, Pandas, scikit-learn; PyTorch or TensorFlow
• Analytical rigor — comfort forming and testing hypotheses against messy, imperfect real-world data

Good to Have

• Time-series / signal processing exposure (filtering, windowing, frequency analysis)

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  Get Hired
• Edge/mobile ML deployment (TensorFlow Lite / ONNX)
• Prior work with IMU, GPS, or embedded sensor data
• Git and collaborative workflows (Jira)

Who You Are

A builder, not a tutorial-follower. Youve trained models yourself, debugged why they didnt work, and can explain every design choice you made. Youd rather dig into messy real-world data than tune a pre-built model on Kaggle — and youre comfortable saying the data doesnt support that assumption and backing it up.

What Youll Gain

• Direct ownership of models that ship into a live consumer product
• Mentorship from engineers with automotive, embedded, and applied ML backgrounds
• End-to-end ML exposure: from raw sensor data to production detection logic