About VistralNova

VistralNova is a technology company building developer tools, game systems, AI-powered experiences, and digital infrastructure for modern interactive products. Our work brings together software engineering, game design, artificial intelligence, and asset-ready infrastructure to help teams create products that are practical, scalable, and easier to trust.

We are evolving toward a flexible technical foundation aligned with Polkadot and PVM-based architecture. Our team values clean architecture, practical execution, strong engineering standards, and long-term product quality.

About the Role

The AI Systems Engineer builds the intelligent systems layer behind VistralNova's games and digital products — from adaptive gameplay and simulation-driven features to backend AI services that support product and operations teams. This role is about applying practical machine learning and AI engineering to real product problems, not research for its own sake. You'll work closely with game designers, backend engineers, and product to figure out where AI genuinely improves the experience and ship it reliably.

What You'll Do

Design and build AI/ML-driven features for games and product systems (e.g. adaptive difficulty, simulation, recommendations).
Build and maintain backend services that expose AI capabilities to other systems.
Evaluate, fine-tune, or integrate third-party models where appropriate rather than building everything from scratch.
Work with game design to identify where AI improves player experience without adding unnecessary complexity.
Monitor model performance, cost, and reliability in production.
Document system behavior and limitations clearly for other engineers and stakeholders.

What We Look For

Practical experience building and shipping AI/ML-powered features in production systems.
Strong backend engineering skills to integrate AI systems into real products.
Experience with modern ML/AI tooling (e.g. Python ML stack, LLM APIs, vector search) as applicable.
Ability to make pragmatic build-vs-integrate decisions rather than defaulting to custom models.
Good judgment about cost, latency, and reliability tradeoffs in AI-powered features.
Clear communication with non-ML engineers, designers, and product stakeholders.

Nice to Have

Experience with simulation-driven or adaptive game systems.
Familiarity with on-chain-connected or digital asset products.
Experience with LLM-based agents, tool-use, or retrieval-augmented systems.
Comfort operating in a small, fast-moving startup environment.