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.