Machine Learning Systems Engineer
Tasks
- Architect scalable backend services for media intelligence platform
- Build distributed event driven media processing workflows
- Build retrieval workflows for semantic search and media discovery
- Collaborate with ML and engineering teams to deliver AI powered features
- Deploy and operate systems on cloud infrastructure
- Design APIs and processing pipelines for multimedia ingestion
- Design and maintain vector search and indexing systems
- Develop and optimize metadata extraction and content analysis workflows
- Ensure reliability with observability and incident response
- Evaluate model performance and safely roll out upgrades
- Implement asynchronous processing patterns and fault tolerance
- Integrate model APIs into backend inference workflows
- Maintain model serving systems and GPU utilization
- Mentor engineers and improve engineering quality
- Monitor production performance metrics and costs
- Optimize inference workflows for latency throughput reliability and cost
- Own critical backend components end to end
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon SageMaker | Asynchronous processing | Autoscaling | Azure | Batching | Caching | Data Modeling | Distributed Systems | Docker | Elasticsearch | Embeddings | Event Driven | Event-driven architecture | Faiss | GCP | GPU Utilization | Hugging Face | Hugging Face Transformers | Indexing | JavaScript | Knowledge Distillation | Kubernetes | Logging | Message Queues | Metrics | Microservices | Model Optimization | NVIDIA Triton | NoSQL | OCR | Observability | PGVector | Pinecone | Prompt Optimization | PubSub | PyTorch | Python | Qdrant | Quantization | REST APIs | Retrieval-Augmented Generation | SQL | Scene detection | Schema Design | Semantic Search | Similarity Search | Speech Recognition | Speech-to-Text | System design | TGI | TensorFlow | Tracing | Transcription | VLLM | Vector Search | Vertex AI | Weaviate
Education
N/A
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