Lead Software Engineer

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

At

Cadence

, we hire and develop leaders and innovators who want to make an impact on the world of technology. We are a global leader in electronic design automation (EDA), enabling the design of cutting-edge semiconductors and system-level products.

Within Cadence, the Virtual Protocols Group is building next-generation Accelerated Verification IP (AVIP) using AI-driven methodologies to dramatically improve development velocity, quality, and scalability. At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

The Cadence Advantage

• The opportunity to work on cutting-edge technology in an environment that encourages you to be creative, innovative, and to make an impact.
• Cadence’s employee-friendly policies focus on the physical and mental well-being of employees, career development, providing opportunities for learning, and celebrating success in recognition of specific needs of the employees.
• The unique “One Cadence – Team” culture promotes collaboration within and across teams to ensure customer success
• Multiple avenues of learning and development available for employees to explore as per their specific requirement and interests
• You get to work with a diverse team of passionate, dedicated, and talented individuals who go above and beyond for our customers, our communities, and each other—every day.

Position Overview

We are looking for a Software Engineer with 3+ years of relevent experience to contribute to the development of next-generation AI-driven engineering systems for AVIP development. You will work closely with architects and senior engineers to build scalable, production-grade systems combining AI and EDA technologies.

Desired Attributes

• Sincerity, aptitude to be able plan and solve large scale problems and attention to detail
• Curiosity to explore emerging technologies (AI, EDA, systems)
• Agility to work in a fast-paced, high-visibility | high-impact environment.
• Willingness to learn cutting across domains (software + hardware + AI)

• This role spans:

• Core software development.
• Generative AI engineering.
• Developer productivity tooling.
• High-performance systems programming.

Key Responsibilities

• Core Software Development

  • Design and implement features in:

    • AI-driven development frameworks
    • Developer tools and automation systems
    • Develop reusable libraries and utilities for internal engineering workflows
    • Write high-quality, maintainable, and efficient code
    • C++ based GUI/CLI/TUI applications

  • Generative AI & LLM Engineering

    • Contribute to systems involving: Model fine-tuning and adaptation, Prompt engineering and model steering, Evaluation, and benchmarking of LLM outputs
    • Build and maintain training data pipelines, including Data generation, Data cleaning and pruning, Dataset validation
    • Support development of domain-specific AI systems for: Code generation, Specification understanding, Validation automation

  • Agentic Workflow Development

    • Work on multi-agent systems for: Task decomposition, Code generation, Self-validation and debugging loops
    • Contribute to orchestration frameworks that connect: LLMs, Developer tools, Runtime environments

  • Systems & Performance Engineering

    • Contribute to performance-critical components using:

      • Modern C++ (C++20/23/26)
      • Low-level performance optimisation techniques
      • Work with GPU acceleration where applicable

• Validation & Quality

  • Develop and maintain: Unit tests, Integration tests, Validation flows
  • Participate in: Code reviews, Debugging and root-cause analysis, ensure engineering quality aligned with production standards

• Collaboration

  • Work closely with:

    • Cross Geography AVIP development teams
    • AI/ML engineers
    • Verification and validation engineers

  • Quickly learn domain concepts related to:

    • High Speed Serial and Bus Protocols
    • Hardware-software co-design
    • AI-enabled tooling

Required Qualifications

• Master’s degree or Bachelors In:

  • Computer Science with AI focused projects
  • Electronics/Electrical Engineering with Embedded System/HPC/Device Driver Development focussed projects

• Strong programming skills in:

  • C++ and/or Rust/Python

• Solid fundamentals in:

  • Data structures and algorithms
  • Software design and debugging

• Familiarity with:

  • Linux/Unix development environments
  • Git/Perforce-based workflows

• Experience or coursework in:

  • Compiler/tooling ecosystems (e.g., Clang, language servers)
  • Hardware description languages (SystemVerilog/SystemC)
  • Parallel/GPU computing

Behavioral skills required

• Must possess strong written, verbal and presentation skills
• Ability to establish a close working relationship with both peers and management.
• Explore what’s possible to get the job done, including creative use of unconventional solutions.
• Work effectively across functions and geographies
• Push to raise the bar while always operating with integrity.

We’re doing work that matters. Help us solve what others can’t.

• Sincerity, aptitude to be able plan and solve large scale problems and attention to detail
• Curiosity to explore emerging technologies (AI, EDA, systems)
• Agility to work in a fast-paced, high-visibility | high-impact environment.
• Willingness to learn cutting across domains (software + hardware + AI)
• Master’s degree or Bachelors In: Computer Science with AI focused projects Electronics/Electrical Engineering with Embedded System/HPC/Device Driver Development focussed projects
• Strong programming skills in: C++ and/or Rust/Python
• Solid fundamentals in: Data structures and algorithms Software design and debugging
• Familiarity with: Linux/Unix development environments Git/Perforce-based workflows
• Experience or coursework in: Compiler/tooling ecosystems (e.g., Clang, language servers) Hardware description languages (SystemVerilog/SystemC) Parallel/GPU computing