AI Engineer

At Dyssonance, we’re building post-LLM AI systems that reason under uncertainty, evolve their internal models of the world, and maintain coherence over time rather than rely on static knowledge.

We are a small, elite founding team from Deep Mind, Google, Salesforce, Apple, and venture-backed startups, combining frontier research with production-grade engineering. Our work sits at the intersection of reasoning systems, dynamic memory, and large-scale AI infrastructure.

We’re looking for AI Engineers who enjoy building real systems around hard problems and who genuinely care as much about how intelligence works as they do about writing clean, reliable code.

This role sits between research and systems engineering. You will work closely with researchers to transform new ideas into working systems, helping define how next-generation AI actually runs in production.

Location: Los Altos, CA / Hybrid

Team: Engineering

Type: Full-Time

• Build production systems that integrate machine learning models into larger reasoning and decision-making architectures.
• Implement infrastructure supporting model execution, state management, and iterative reasoning workflows.
• Work closely with researchers to translate experimental ideas into reliable, maintainable components.
• Design and implement APIs, services, and system components that support evolving AI behavior.
• Build tooling and infrastructure for experimentation, evaluation, and rapid iteration.
• Improve performance, reliability, and observability across AI system components.
• Contribute to architectural decisions around system composition, data flow, and model interaction.
• Help define how experimental AI systems transition from prototype to production.

• Strong software engineering fundamentals and experience writing production-quality code.
• Experience working with machine learning systems beyond model training alone.
• Strong Python skills and comfort working across ML and backend systems.
• Ability to reason about system behavior, performance, and failure modes.
• Comfort working in environments where requirements evolve quickly.
• Intellectual curiosity about AI systems and how intelligent behavior emerges from system design.
• Ability to collaborate closely with researchers and iterate rapidly on new ideas.
• Strong ownership mindset and ability to move from idea to deployed system.

• Experience building ML infrastructure, inference systems, or agent-based systems.
• Familiarity with probabilistic modeling or uncertainty-aware systems.
• Experience with graph-based representations or structured knowledge systems.
• Experience supporting experimentation platforms or research workflows.
  
  Background in distributed systems or performance-sensitive environments.

• Founding-level impact on the architecture of a new AI paradigm.
• Work directly with researchers shaping next-generation reasoning systems.
• Operate at the boundary between research and real-world systems.
• Join a team that values intellectual depth as much as engineering excellence.