Mploy - דרושים
Mploy - דרושים

דרושים Machine Learning Engineer בתל אביב

 \ 

Machine Learning Engineer

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04/11/2025

 תל אביב

 Unleash

**We are a dynamic startup located in the heart of Tel Aviv, seeking a Machine Learning Engineer to join our Data Science team.

You will play a key role in developing, productionizing, and maintaining the Data Science components that power our Enterprise AI experience.

**Why Unleash?

**We're building an AI-driven, personalized workspace application. Our solution blends natively into the user's workflow and allows the user to find, organize, access, and act on resources from all the different SaaS applications they are connected to, rapidly and efficiently.

What You'll Do?

  • Design and implement scalable machine learning pipelines that power our AI search capabilities
  • Productionize and optimize RAG (Retrieval-Augmented Generation) components, embedding models, and related data services
  • Work in multi-threaded and high-concurrency environments, ensuring system robustness and efficiency
  • Identify, analyze, and resolve performance bottlenecks, improving latency, throughput, and overall system reliability
  • Collaborate closely with data scientists to transform research prototypes into robust, production-ready systems
  • Work in a small and intimate R&D team in TLV

Requirements:

**Requirements

**We want to hear from you if you have:

  • 3+ years of experience in software engineering or machine learning engineering
  • Strong programming skills in Python- Must
  • Proven experience taking ML models or pipelines to production
  • Hands-on experience with RAG pipelines, vector databases, or embedding-based retrieval
  • Experience working with modern ML frameworks (e.g., PyTorch, TensorFlow)
  • Familiarity with cloud environments (AWS, GCP, or Azure)
  • Experience with containerized deployment (Docker, Kubernetes) - advantage
  • Familiarity with LangChain, LlamaIndex, or similar LLM orchestration frameworks - advantage
  • Experience with CI/CD and software best practices for ML systems - advantage

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