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