Senior Machine Learning Engineer
Senior Machine Learning Engineer
Warner Bros. DiscoveryThis job is no longer accepting applications
Job Description
Roles & Responsibilities
- The role will focus on building out machine learning solutions for WBDs content and Ad platform. Primary focus will be on unlocking machine learning opportunities in media supply chain, captioning services, ad conditioning, metadata extraction and building foundational machine learning training and inference pipelines at scale.
- You have a successful track record for ambitious projects across cross-functional teams. You are passionate and results oriented. You strive for technical excellence and are very hands-on. Your co-workers love working with you. You have built respect in your career through concrete accomplishments.
- Build cutting-edge capabilities utilizing machine learning and data science (e.g., large language models, computer vision models, advanced ad & content targeting, etc.)
- Build end to end ML pipelines to train, deploy and server ML models at scale.
- Leverage industry best practices and tools to continually improve teams ability to build, operate and maintain products.
- Ensure that technical solutions are in line with established WBD Digital strategy, standards in respect to architecture, security, corporate governance, coding standards, monitoring, logging, unit test, and service enablement.
What to Bring
- 5-8 years of experience designing, building machine learning algorithms and systems.
- Proficiency in programming languages such as Java, Golang, Python or Scala.
- Working experiemce in AWS or GCP and ML platforms like sagemaker or Vertex AI.
- Good understaning of distributed systems, alogorithms and data structures.
- Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow.
- Strong understanding of modern ML approaches (GBDT, CNN, LSTM, GRU, HRNN, transformers, variational auto-encoders, ...).
- Experience using of ML tools and frameworks (TensorFlow, Keras , pyTorch , scikit-learn, Spark,..).
- Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring).
- Experience with offline experimentation and A/B testing.
- Understanding of batch and streaming data processing techniques.
- PhD or Masters in Computer Science or related discipline.
More Info
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About Company
Skills:
error analysis , Tensorflow, Pytorch, Docker, Python, LangChain, Evaluation metrics, pgvector, Qdrant, Statistical testing, Container orchestration, MLflow, Pinecone, LLM orchestration frameworks, API integrations, Cloud deployments, Vector search engineering, LlamaIndex, MLOps fundamentals
Skills:
Svm, XGBoost, Python, Decision Trees, Clustering, MLops, Restful Apis, Agent reasoning patterns, Prompt engineering, Feature selection, scikit-learn, RAG systems, Data preprocessing, Conversation analytics, AI system observability frameworks, LLM conversational AI systems, Feature engineering, semantic search, AWS Sagemaker, LangChain, CrewAI, Vector databases, LightGBM, Ensemble models, Regression, Statistical Modeling, Agent frameworks, Context optimization
Skills:
Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow
Skills:
Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning
Skills:
data engineering , model selection , Numpy, Pandas, Pytorch, Python, scikit-learn, ELT data pipelines, ML research methodologies, Production ML systems, ML pipelines, Hypothesis formulation and testing, Cross-validation, Performance benchmarking, Reproducible research