Machine Learning Systems Engineer
Tasks
- Architect scalable backend services
- Build event-driven workflows
- Build vector search and indexing systems
- Collaborate with ML and product teams
- Deploy and operate systems on cloud platforms
- Design processing pipelines for media ingestion
- Design semantic retrieval and duplicate detection
- Ensure reliability with observability and incident response
- Implement asynchronous job execution
- Implement model optimization techniques
- Integrate AI ML workflows into production systems
- Mentor engineers and lead technical discussions
- Optimize metadata extraction and transcription pipelines
- Optimize model inference latency and throughput
- Own backend components end-to-end
- Serve models using inference platforms
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SQS | Amazon SageMaker | Autoscaling | Azure | Batching | Caching | Distributed Systems | Faiss | GPU Utilization | Google Cloud | Google Pub/Sub | Hugging Face | Inference Server | Kafka | Logging | Metrics | Model Distillation | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | NoSQL | Node.js | OCR | Observability | PGVector | Pinecone | Prompt engineering | Pub/Sub | PyTorch | Python | Qdrant | Quantization | REST API | SQL | SageMaker | Semantic Search | Speech-to-Text | System design | TensorFlow | Text Generation | Text generation inference | Tracing | Transformers | Triton Inference Server | VLLM | Vector Search | Vertex AI | Weaviate
Education
N/A
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