AI Researcher

Description

Location: Ra'anana

#Hybrid

DriveNets is looking for an AI Research Lead to join its AI-OPS project and a successful software team as part of the new and rapidly evolving project. If you're passionate about driving cutting-edge AI innovation while coordinating with multidisciplinary R&D teams, we'd love to hear from you.

Responsibilities

• Set the technical vision and roadmap for AI research initiatives aligned with organizational goals. Oversee the full lifecycle of AI model development, from ideation to deployment.
• Mentoring researchers/scientists, fostering collaboration and professional growth. Conduct performance reviews and resolve technical blockers.
• Finetune novel LLM algorithms for domain-specific applications.
• Evaluate emerging AI trends and recommend adoption pathways.
• Oversee and contribute to developing novel classical AI algorithms and techniques to enhance machine learning results.
• Work collaboratively with cross-functional teams, including Software engineers, DevOps, Network Architects, and Product Managers, to align development with business goals.
• Ensure research outcomes are practical, scalable, and can be transitioned into production.
• Represent the AI team in technical discussions, stakeholder meetings, and conferences.
  
  

• Master’s holders: At least 5+ years of experience in AI/ML research & development.
• Strong background in machine learning, deep learning, and AI algorithms.
• Hands-on experience with deep learning frameworks such as PyTorch and TensorFlow, as well as libraries like Hugging Face Transformers.
• Proficiency in Python, C++, or other relevant programming languages.
• Experience in GPU acceleration (e.g., CUDA, Triton, TensorRT, vLLM) and scalable AI model fine-tuning.
  
  

• PhD in AI, Computer Science, Engineering, or related field
• Published research in AI/ML conferences
• Experience with AI APIs, prompt engineering, Multi-Agent architectures, retrieval-augmented generation (RAG), LoRA, or AI-Ops
• Experience with data stream processing pipelines and data analytics
• Knowledge of Docker and Kubernetes for containerization and orchestration
• Familiarity with CI/CD pipelines and MLOps tools such as Jenkins, GitHub Actions, GitLab CI, or MLflow for model deployment and monitoring
• Experience with computer networks (e.g., CCNA/CCNP level)