Qrata
Website:
qrata.ai
Job details:
Role - Senior Applied ML Engineer
Location - Bangalore, Karnataka
Experience - 6-10 yrs
Role Overview
You will build computer-vision and multimodal systems that understand real-world video.
You will own the full model lifecycle: problem definition, data strategy, experimentation, evaluation,
production launch, and post-launch improvement. Success means reliable production behavior — not benchmark performance or impressive demos alone.
Must Have - Build Computer-Vision and Multimodal systems
What You'll Work On:
● Face, person, object, and sensitive-text detection
● Object and person tracking
● Temporal event and action recognition
● Video quality assessment
● Evidence extraction and video summarization
● Multimodal video understanding
What You'll Do:
● Build representative training and evaluation datasets from field footage
● Design annotation guidelines, sampling strategies, hard-negative mining, and active-learning
workflows
● Define model metrics connected to product outcomes: privacy-critical false negatives, precision
and recall, confidence calibration, human-review burden, and performance across operating
conditions
● Establish strong baselines, experiment tracking, model cards, and launch criteria
● Diagnose failures frame by frame and convert patterns into data, modeling, or product
improvements
● Use production failures and reviewer feedback to improve datasets and models
● Work with the ML Evaluation engineer to define quality standards and regression tests
● Work with backend and platform engineers to package, deploy, monitor, and roll back models
● Communicate model limitations, uncertainty, and trade-offs clearly
What We're Looking For
● Strong Python and PyTorch experience
● Experience shipping computer-vision models into production
● Strong foundation in several areas: object detection and segmentation, OCR, multi-object
tracking, action recognition, temporal localization, and video or vision-language models
● Strong understanding of precision/recall trade-offs, calibration and threshold selection, dataset
leakage, label quality, distribution shift, and stratified evaluation
● Experience building datasets and evaluation systems for messy real-world inputs
● Ability to independently own ambiguous ML problems from framing through production
● Strong software-engineering fundamentals
● Ability to explain complex model behavior to product and operations teams
Click on Apply to know more.