Machine Learning Systems Engineer
Tasks
- Apply model optimization techniques like quantization and batching
- Architect scalable backend services
- Build high throughput media ingestion pipelines
- Build semantic search similarity matching and duplicate detection retrieval
- Deploy and operate systems on cloud platforms
- Design and maintain vector search and indexing systems
- Design data models for media assets and metadata
- Develop metadata extraction and transcription workflows
- Ensure reliability with logging metrics tracing and alerting
- Evaluate models and safely roll out upgrades
- Implement asynchronous processing with retries idempotency and dead letter queues
- Implement event driven workflows with queues and pub sub
- Integrate AI ML services into backend workflows
- Monitor performance and costs in production
- Optimize inference for latency throughput reliability and cost
- Own backend components end-to-end
- Serve models using inference servers and GPU utilization tuning
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon SageMaker | Asynchronous processing | Azure | Backpressure | Batching | Caching | Dashboards | Data Modeling | Dead Letter | Dead Letter Queue | Distributed Systems | Elastic Vectors | Embeddings | Event Driven | Event-driven architecture | Faiss | GCP | GPU Utilization | Hugging Face | Hugging Face Transformers | Idempotency | Inference Server | Logging | Metrics | Microservices | Model Distillation | Model Serving | Multimodal inference | NoSQL | Node.js | OCR | Operational runbooks | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PyTorch | Python | Qdrant | Quantization | Queues | REST APIs | Retries | SQL | Scene detection | Semantic Search | Similarity Matching | Speech Recognition | Speech-to-Text | System design | TGI | TensorFlow | Tracing | Triton Inference | Triton Inference Server | VLLM | Vector Search | Vector indexing | Vertex AI | Weaviate
Education
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