Machine Learning Engineer
Tasks
- Build asynchronous processing systems for reliability and efficiency
- Design and build ML inference systems
- Design scalable CPU GPU data processing pipelines
- Enable deployment and operation of ML systems with MLOps
- Implement observability metrics logging tracing
- Integrate hybrid search embedding reranking evaluation
- Iterate on models with data scientists and product teams
- Monitor and debug model latency memory throughput
- Optimize inference latency and throughput at scale
- Productionize and optimize model inference pipelines
Perks/Benefits
- Company paid life insurance
- Medical, dental, and vision coverage
- Mental well-being resources
- Mentorship and resources
- Paid parental leave
- Paid vacation and sick days
- Professional development opportunities
- Retirement benefits
Skills/Tech-stack
AWS Batch | AWS EC2 | AWS Lambda | Airflow | Async Processing | BM25 | Caching | Cloud Computing | Compilation | Cross-Encoder | Deduplication | Deep learning | DistilBERT | Distillation | Distributed inference | Embedding Generation | Entity recognition | FFmpeg | FastAPI | Fault Tolerance | HNSW | Huggingface | MLOps | Machine Learning | Model Inference | Named Entity Recognition | ONNX | ONNX Runtime | OpenAI API | PyTorch | Quantization | Reranking | SBERT | SageMaker | SavedModel | TFLite | TensorFlow | Tf data | TorchScript | Torchserve | Transformer | Vector Search | Video Shot Boundary Detection | XLA
Education
N/A
Regions
Countries
States
Cities
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