Senior Machine Learning Engineer- Computer Vision

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

Who We Are…

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

Machine Learning Engineer – Services (Video AI Platform) 
 

Who We Are… 
At Warner Bros. Discovery, we are reimagining how machine learning transforms storytelling. As part of the AI/ML organization, focusing on supporting applications of AI to video, the Machine Learning Engineer – Services group powers infrastructure and backend services behind production workflows. We're looking for an experienced ML Engineer with strong fundamentals and infrastructure experience to help build reusable components and services for video understanding, video summary, and video classifications.  

You will be part of a team focused on re-training, model hosting, cost optimization, and managing production workflows at scale. 

Roles & Responsibilities 

• Build and maintain pipelines for model fine-tuning and retraining, including LoRA-based workflows and Large Language Models

• 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 machine learning engineering, with end-to-end ML workflow expertise 

• Strong background in model retraining, fine-tuning, and evaluation techniques 

• 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 

• Deep understanding