Senior Applied ML Engineer

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 behaviour, not benchmark performance or impressive demos alone.
Responsibilities:
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, modelling, 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.
Requirements:
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 localisation, 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 behaviour to product and operations teams.

Experience
6-10 yrs