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