Machine Learning Engineer

Engramme builds machine learning systems that support memory retrieval and contextual understanding. This onsite role in San Francisco, CA focuses on taking applied ML from model development to production-grade infrastructure, with an emphasis on scalable deployment, monitoring, and real-time performance.

What you’ll work on

• Design and implement ML models for memory retrieval and ranking
• Build ML infrastructure and training pipelines that can scale
• 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, including monitoring and operational workflows
• Collaborate with the research team to productionize new algorithms

Core requirements

• 4+ years of ML engineering experience
• Strong proficiency in Python and ML frameworks, including PyTorch and TensorFlow
• Experience deploying ML models to production
• Knowledge of MLOps, model serving, and monitoring
• Understanding of NLP, embeddings, and retrieval systems
• Experience with AWS and/or GCP plus containers
• Strong software engineering fundamentals
• Experience with large-scale data processing

Tools you’ll use

Python, PyTorch, TensorFlow, AWS, GCP, containers, vector databases, embedding systems

Nice to have

• 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 ML systems
• Familiarity with transformer architectures