Applied AI Engineer

This is a fully remote position, open to applicants in Arizona, +3 more states.

📋 Description

• Design and implement AI systems that enhance post-silicon validation, making it more efficient, intelligent, and scalable across semiconductor settings.

• Collaborate closely with cross-functional engineering teams to discover AI opportunities and develop solutions.

• Assess new AI frameworks and architectures, providing recommendations for valuable technologies.

• Create data systems to evaluate the impact of AI initiatives.

• Set quantitative metrics, address performance deficiencies, and foster continuous improvement.

• Design and execute AI solutions within the design and automation framework for NVIDIA chips.

• Drive projects from initial concept through to deployment.

⛳️ Requirements

• BS, MS, or PhD, or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or a related discipline.

• Over 5 years of practical experience in building and deploying ML/AI systems or data-intensive backend services.

• At least 2 years of hands-on Applied AI experience, managing an AI agent, LLM-powered workflow, or intelligent automation system from start to finish.

• Proficient in Python programming.

• Skilled in at least one static programming language such as C, C++, C#, Java, or Scala.

• Solid understanding of electrical engineering principles, including computer architecture, high-speed interfaces, timing, power fundamentals, firmware/driver structures, and hardware interactions.

• Practical experience in production testing, system validation, post-silicon bring-up, reliability assessment, silicon debugging, or silicon productization, including ATE, SLT, board-level testing, validation, or yield analysis.

• Knowledge of silicon development environments and familiarity with chip and system characterization methodologies.

• Understanding of manufacturing and quality metrics such as yield, FPY, DPPM, RAS, TTR, and escape rate.

• Demonstrated ability to manage multiple projects simultaneously.

• Exposure to GPU, CPU, AI accelerators, networking, automotive, or large-scale SoC programs.

• Familiarity with contemporary AI technologies and methodologies for developing and launching LLMs.

• Experience in building and deploying orchestration agents that manage hundreds to thousands of tools.

• Ability to convert AI research into practical production tools.

• Experience with PyTorch or TensorFlow and orchestration tools like NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.

🏝️ Benefits

• Equity

• Comprehensive benefits package

• Competitive salary structure