Role description
EngrammeBuild and deploy ML models for memory retrieval and contextual understanding. Create scalable ML infrastructure that powers intelligent memory systems.
What You'll Do
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
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
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