Machine Learning Engineer

at  Qdrant

We are developing Qdrant, an open-source vector database that enables developers to use cutting-edge, blazing-fast vector similarity search technology. Our flagship product is the open-source Vector Similarity Search Engine: https://github.com/qdrant/qdrant Our mission is to provide a scalable, cost-effective, open-source solution that simplifies the development of applied-AI applications. We are a fast-growing startup, and we are looking for a Middle+/Senior Machine Learning engineer to join our team.
Tasks

The role of an ML Engineer in the Qdrant ecosystem team differs from that of a typical ML Engineer position: you may not need to train models or build ML pipelines on a daily basis. Instead, the focus is on the development of tools that help other developers, ML engineers, and practitioners build their AI solutions using Qdrant products. Tasks that a successful candidate will work on include:

• Work on Python-based libraries that extend the Qdrant ecosystem. Examples:
• https://github.com/qdrant/fastembed
• https://github.com/qdrant/qdrant-client
• Develop Qdrant integration with other projects and frameworks.
• Consult our users on best practices building performant vector search solutions.

Requirements

• Proven Python experience of 3+ years, including multiprocessing, PyTorch, Pydantic, etc.
• Experience with model inference in production. ONNX, triton, tensor-rt, etc.
• Practical experience with developing vector search applications.
• Familiarity with Machine Learning concepts like embeddings, metric learning, LLMs

• Work on Python-based libraries that extend the Qdrant ecosystem. Examples:
• https://github.com/qdrant/fastembed
• https://github.com/qdrant/qdrant-client
• Develop Qdrant integration with other projects and frameworks.
• Consult our users on best practices building performant vector search solutions