Senior Staff Software Engineer, Cognitive Architecture, Special Projects

Google Google · Big Tech · Mountain View, CA +1

Senior Staff Software Engineer role focused on mechanistic interpretability research for AI safety and alignment. The role involves designing and implementing cognitive architecture primitives for future agentic AI systems, guiding their development using agentic programming tools, and serving as a technical leader. Experience in speech/audio, reinforcement learning, ML infrastructure, or other ML fields is required, along with ML design and infrastructure experience.

What you'd actually do

  1. Design, architect, and implement cognitive architecture primitives that form the foundation of future agentic artificial intelligence systems.
  2. Guide the development and integration of these cognitive systems, leveraging agentic programming tools (e.g., Antigravity/Jetski, full-stack vibe coding) to accelerate production and deployment.
  3. Partner with stakeholders, product managers, and research scientists to ensure system implementation is aligned with objectives and ethical guidelines.
  4. Serve as a technical leader, mentoring engineers and researchers, and setting standards for engineering excellence and scalable artificial intelligence systems.
  5. Deliver continuous improvement in core infrastructure and development workflows, and serve as a team foundation on scaled evaluation and the piloting of artificial intelligence capabilities.

Skills

Required

  • software development
  • software design and architecture
  • ML design
  • ML infrastructure
  • speech/audio
  • reinforcement learning
  • machine learning infrastructure

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • technical leadership role
  • working in a matrixed organization

What the JD emphasized

  • mechanistic interpretability
  • reverse-engineer the internal computations of large language models
  • safety, alignment, and reliability
  • agentic artificial intelligence systems
  • speech/audio
  • reinforcement learning
  • machine learning infrastructure
  • ML design and ML infrastructure

Other signals

  • mechanistic interpretability
  • agentic artificial intelligence systems
  • reverse-engineer the internal computations of large language models