At least 4 years of machine learning engineering experience.
Strong proficiency in Python and machine learning frameworks such as PyTorch and TensorFlow.
Experience deploying machine learning models to production.
Knowledge of MLOps, model serving, and monitoring.
Understanding of natural language processing, embeddings, and retrieval systems.
Experience with cloud platforms such as AWS or GCP and containers.
Strong software engineering fundamentals.
Experience with large-scale data processing.
Responsibilities
Design and implement machine learning models for memory retrieval and ranking.
Build machine learning 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 the research team to productionize new algorithms.
Desired Qualifications
Experience with large language models and prompt engineering.
Knowledge of vector databases such as Pinecone, Weaviate, or Milvus.
Experience with recommendation systems or search.
Background in information retrieval or ranking systems.
Knowledge of model optimization and quantization.
Experience with real-time machine learning systems.
Familiarity with transformer architectures.
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