Machine Learning Systems Engineer
Tasks
- Apply model optimization techniques including quantization and batching
- Architect scalable backend services for media intelligence platform
- Build high throughput media ingestion and processing pipelines
- Build retrieval workflows for semantic search and similarity matching
- Collaborate with teams and mentor engineers
- Deploy and operate systems on cloud platforms with observability and incident response
- Design and maintain vector search and indexing systems
- Design data models and storage patterns for media assets and metadata
- Develop and optimize metadata extraction and content analysis workflows
- Implement event driven workflows using queues and pub sub
- Implement reliable asynchronous processing patterns
- Integrate AI ML services into backend workflows
- Monitor production performance and reliability metrics
- Optimize inference workflows for latency throughput reliability and cost
- Own backend components end-to-end
- Serve and optimize models using model serving systems
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon SQS | Amazon SageMaker | Approximate Nearest Neighbor | Asynchronous processing | Azure | Backpressure | Batching | Caching | Cloud platform | Dashboards | Data Modeling | Dead Letter | Dead-letter queues | Distributed Systems | Elastic Vectors | Embeddings | Event Driven | Event-driven architecture | Faiss | Google Cloud | Google Cloud Platform | Google Pub/Sub | HuggingFace Transformers | Idempotency | Inference Server | Kafka | Logging | Metrics | Microservices | Model Distillation | Monitoring | Nearest neighbor | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PyTorch | Python | Qdrant | Quantization | REST APIs | Retries | SQL | Semantic Search | Speech-to-Text | System design | TensorFlow | Text Generation | Text generation inference | Tracing | Triton Inference | Triton Inference Server | VLLM | Vector Search | Vertex AI | Weaviate
Education
N/A
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