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Warner Bros. Discovery
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Senior Data Scientist - Video AI team,

Senior Data Scientist - Video AI team,

Warner Bros. Discovery

5 - 10 years

50 - 55 Lacs

bengaluru

Posted:1 hour ago| Platform:

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Skills Required

training computer vision backend debugging machine learning workflow open source gaming cost monitoring

Work Mode

Work from Office

Job Type

Full Time

Job Description

Ship orchestrated pipelines for training, batch/stream inference, and evaluation with lineage, versioning, and observability.
Design, build, and operate reusable services for training, batch/online inference, and vector/lexical search that meet clear SLOs.
Build/own vector + lexical search stacks and indexes; manage ingestion, filters, caching, and re-ranking.
Build/own vector + lexical search stacks and indexes; manage ingestion, filters, caching, and re-ranking.
Create reliable data/feature and evaluation pipelines with strong lineage, versioning, and observability.
Implement model deployment/rollout patterns (canary/AB, shadow) with monitoring and fast rollback.
Drive cost, reliability, and performance improvements across GPU/CPU workloads.
Integrate and maintain vector search services and semantic similarity infrastructure
Design scalable model serving solutions for open-source and foundation models
Develop systems for experiment tracking, model versioning, and evaluation
Monitor production models for drift and performance degradation
Manage compute cost and resource optimization across distributed training jobs
Integrate Human-in-the-Loop (HITL) workflows and offline labeling into training pipelines
Support model deployment for varied model architectures, including Vision-Language Models, Convolutional Neural Nets, and Embedding Generation models
Stand up and maintain Feature Store and data versioning infrastructure
Architect and implement RAG pipelines for video metadata, summarization, and Q&A
Build evaluation frameworks to assess LLM performance, hallucination frequency, and structured response accuracy

What to Bring :

5+ years of experience in Applied science, with end-to-end ML workflow expertise

Strong background in model retraining, fine-tuning, and evaluation techniques
Research work and having publications/patents in some conferences.
Computer vision experience is a must for Applied Research role
Suggest and develop models

Experience owning production ML or data services end-to-end (design deploy operate) with strong debugging and on-call instincts.

Familiarity with modern orchestration and CI/CD for ML, containerized serving, and scalable storage/indexing.

Pragmatic approach to telemetry (traces, metrics, logs) and using it to improve quality, latency, and cost.

Strong collaboration with ML researchers/engineers and product teams.

Experience deploying and managing open-source model servers (e.g., Triton, TorchServe, Ray Serve)

Proficient in managing cost-effective distributed computing environments (e.g., Kubernetes, Ray, SageMaker)

Familiar with experiment tracking tools (e.g., MLflow, Weights & Biases) and model versioning strategies

Experience with real-time inference systems and streaming data pipelines is a plus

Familiarity with labeling tools, HITL workflows, and offline data curation strategies

Comfort working in Agile development environments and collaborating across global teams
Trains/finetunes video & image models, builds data pipelines, writes clean PyTorch/JAX, adds tests & ablations.
Practical experience with training curves, hyperparam search, model distillation/quantization, export to ONNX/TensorRT.
Familiar with modern detectors/segmenters (e.g., SAM/Mask2Former/Grounding-DINO, Retina/CenterNet/YOLOv8+), face detection and recognition models, embedding models.

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