Build and deploy ML models for memory retrieval and contextual understanding. Create scalable ML infrastructure that powers intelligent memory systems.
What You'll Do
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- Design and implement ML models for memory retrieval and ranking
- Build ML infrastructure and training pipelines
- Deploy and monitor models in production at scale
- Optimize model performance and latency for real-time systems
- Work with vector databases and embedding systems
- Implement MLOps best practices and monitoring
- Collaborate with research team to productionize new algorithms
What We're Looking For
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- 4+ years of ML engineering experience
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow)
- Experience deploying ML models to production
- Knowledge of MLOps, model serving, and monitoring
- Understanding of NLP, embeddings, and retrieval systems
- Experience with cloud platforms (AWS, GCP) and containers
- Strong software engineering fundamentals
- Experience with large-scale data processing
Nice to Have
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- Experience with large language models and prompt engineering
- Knowledge of vector databases (Pinecone, Weaviate, Milvus)
- Experience with recommendation systems or search
- Background in information retrieval or ranking systems
- Knowledge of model optimization and quantization
- Experience with real-time ML systems
- Familiarity with transformer architectures
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