Job Description :

This is a model-building role.

You will train, fine-tune, evaluate, and optimize deep learning models that run in production against live traffic. Your day is spent on loss functions, data distributions, decision thresholds, calibration, and inference numerics. It is not spent wiring together third-party APIs or assembling agent frameworks.

Total Experience : 3-6 years

The scale you will operate at :

We run 40+ production ML models across two large families, on a mixed CPU and GPU fleet.

- Documents: Region of Interest detection, Photocopy Classifier, Text Tampering, Photo Tampering, Readability, OCR, Named Entity Recognition, PII Masking.

- Faces: Face Detection, Face Quality, Sunglass Detection, NSFW, Face Mask Detection, Face Match, Liveness Detection, Deepfake Detection.

The production footprint :

- 2000 requests per second at peak

- 25 TB of serving RAM

- 18,000 CPUs

- 120 NVIDIA L4 GPUs

- 2M+ verifications per day

What you will do :

- Frame ambiguous problems mathematically.

- Train models, end to end.

- Evaluate with rigor.

- Optimize for inference at scale.

- Reproduce and improve on research.

- Lead and mentor 3 to 6 junior engineers.

What you bring :

- ML fundamentals: Probability, statistics, optimization, and linear algebra.

- Proven model-training experience in PyTorch.

- Evaluation and calibration expertise.

- 4+ years building and operating large-scale ML systems.

- Working with LLMs at the model level, not just API.