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- Research and prototype LLM-based game generation pipelines (prompting, planning, editing)
- Combine LLMs with classic NLP techniques to improve quality (templates, rule-based checks, post-processing)
- Apply and improve state-of-the-art approaches for code generation and code editing
- Build automated testing + evaluation infrastructure (playability, consistency, determinism, cost/latency)
- Design generation steps in an “Orchestrator → Agents” architecture (code, assets, tests, packaging)
- Reduce generation costs (caching, delta edits, batching, retries, guardrails)
- Work iteratively with the product team: metrics → hypothesis → experiment → shipping
- Hands-on experience with LLMs (code gen, tool-calling, agents, evals)
- Strong PyTorch knowledge (or equivalent deep learning experience)
- Solid foundations in NLP + generative methods
- Production-minded engineering: Git, Docker, Cloud, CI/CD
- Habit of shipping fast prototypes and packaging them into demos
- Game dev experience (especially 2D, web games, Phaser/JS, Unity)
- Asset generation/processing workflows (sprites/animations, audio, simple editor logic)
- Security & sandboxing, prompt-injection defenses, policy-aware design
- Working with product metrics (retention, “fun factor” proxies, funnel optimization)
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