About the role
Build 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