Role : Senior Applied ML Engineer.

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.

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.