Senior Machine Learning Engineer, Content Engineering
Tasks
- Architect vector database integration layers
- Build batch and streaming pipelines
- Build hybrid retrieval systems
- Build offline and online evaluation frameworks
- Design embedding pipelines
- Implement model versioning and embedding migration
- Implement query time optimizations
- Mentor engineers
- Process content with distributed frameworks
- Tune vector quantization strategies
Perks/Benefits
- 401k
- Bonus eligible
- Dental insurance
- Disability insurance
- Life insurance
- Medical insurance
- PTO
- Paid time off
- Tuition assistance
- Vision insurance
Skills/Tech-stack
API Development | Alerting | AlloyDB | Audio Embeddings | Batch Ingestion | Binary Quantization | Caching | Data Pipelines | Distributed Systems | Diversity and Relevance | FFmpeg | Faiss | Filtering | Hybrid retrieval | Image embeddings | Index sharding | Lexical search | Logging | MRR | Machine Learning | Maximal Marginal Relevance | Metrics | Multimodal Learning | NDCG | PGVector | Pinecone | Product quantization | Python | Qdrant | Query Optimization | RAG | Ray | RecallAtK | Reranking | Retrieval-Augmented Generation | Scalar Quantization | Spark | Streaming Ingestion | Vector Databases | Vector Quantization | Vector Search | Vector embeddings | Video Understanding
Education
N/A
Regions
Countries
States
Cities
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