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Machine Learning Engineer
Engineering San Francisco, CA
Build and deploy ML models for memory retrieval and contextual understanding. Create scalable ML infrastructure that powers intelligent memory systems.
PythonPyTorchMLOpsVector DatabasesLLMs
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
About Memorious
We're building a Memory-as-a-Service platform that augments human cognition through ambient intelligence. Our goal is to help people remember everything that matters without the need for active search or prompts.
Have Questions?
Want to learn more about this role or life at Memorious? We'd love to hear from you.
Contact UsReady to Apply?
Send us your resume and a brief note about why you're excited to join Memorious.
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