Senior AI ML Solution Engineer, Ai-native Development

NVIDIA NVIDIA · Semiconductors · Tel Aviv, Israel

Senior AI/ML Solution Engineer focused on designing and building AI-powered development pipelines, evaluating ML approaches for code generation and review, and driving adoption of AI-assisted software development. The role involves architecting feedback and evaluation systems, leading proof-of-concept development, and collaborating on risk-based development levels.

What you'd actually do

  1. Design and build AI-powered development pipelines — from code generation and automated review to feedback loops and evaluation systems.
  2. Evaluate and select ML approaches for specific problems: when to use LLM prompting vs. fine-tuning (QLoRA), classical ML (random forest, linear regression) vs. reinforcement learning, RAG vs. structured extraction.
  3. Architect feedback and evaluation systems that measure and improve AI output quality over time.
  4. Review and refine AI solution architectures — evaluate design decisions, identify weaknesses, propose alternatives with reasoning.
  5. Lead proof-of-concept development to validate new AI/ML approaches for development tooling.

Skills

Required

  • M.Sc. or Ph.D. in Computer Science, Electrical or Computer Engineering (or equivalent experience)
  • 5+ years of industry experience in AI pipelines architecture or related fields
  • Industry experience building and shipping AI-powered tools or ML pipelines
  • Strong understanding of LLM capabilities and limitations — prompt engineering, fine-tuning, RAG, agent architectures
  • Experience with at least two of: reinforcement learning, classical ML, NLP/information retrieval, evaluation framework design
  • Ability to reason about trade-offs
  • Strong programming skills (Python required)
  • Familiarity with ML frameworks — PyTorch, HuggingFace, etc.
  • Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment

Nice to have

  • Experience with LLM-based code generation, code review, or developer tooling
  • Familiarity with eval frameworks and feedback loop design (online and offline evaluation)
  • Experience with AI agent orchestration (multi-agent systems, tool use, planning)
  • Shown research track record (publications, open-source contributions)
  • Knowledge of AI-assisted development tools and their underlying architectures

What the JD emphasized

  • Industry experience building and shipping AI-powered tools or ML pipelines (not just training models — end-to-end delivery).
  • Can reason about trade-offs: when to use which approach, with real reasoning backed by shipping experience.

Other signals

  • AI-powered development pipelines
  • AI-assisted software development
  • LLM capabilities and limitations
  • shipping AI-powered tools or ML pipelines