[Hiring] AI Architect / Tech Lead @Neurons Lab

Role Description

Hands-on Tech Lead for an AI Companion in an online mahjong game. The client is a social gaming company (web3 element) that scales its product and team. We deliver the AI side of their game as their embedded AI partner. The AI Companion plays mahjong at a strong level and explains its moves. The core of the role is to build the mahjong-playing algorithm:

• A dedicated decision-making model (RL, imitation learning, or search-based — trained on the client's hand-history data) with an LLM reasoning layer on top.
• Key design constraints include:
  • A valid-action contract with the game engine (the bridge supplies legal moves)
  • Win detection
  • A 2-second response budget per move
• Explanations run asynchronously.
• Support for more than one rule set (riichi and regional variants) is on the roadmap.

Duration: 3 months, 0.5 FTE.

Qualifications

• Hands-on ML/AI engineering at production scale.
• 6+ years of hands-on ML/AI engineering, with real game AI or sequential decision-making work (RL / MCTS / self-play — not only LLM apps).
• Trained models on user or gameplay data end-to-end (data → training → evaluation → serving).
• Led small delivery teams while still coding personally.
• Comfortable owning an architecture in front of a technical client CTO.

Requirements

• Game AI / sequential decision-making: hands-on RL, imitation learning, or search-based agents (MCTS, self-play) — ideally for imperfect-information games (mahjong, poker, card games).
• Expert Python for ML systems; strong software engineering (APIs, testing, CI).
• Model training on gameplay data end to end: data → training → evaluation → serving.
• LLM application engineering: reasoning layers, prompt and context design, structured outputs, guardrails.
• Low-latency inference: profiling, batching, caching, model-size trade-offs against a hard time budget.
• LLM observability and evaluation (Langfuse or similar).
• AWS deployment for ML workloads.
• Technical leadership of a small pod; clear written and spoken communication with client engineers and executives.

Benefits

• Opportunity to work on cutting-edge AI technology in gaming.
• Collaborative work environment with a focus on innovation.
• Flexible working hours and remote work options.

Questions for Applicants

• Imperfect information: mahjong hides most tiles from each player. How does hidden information change your algorithm choice compared to a perfect-information game like chess?
• Latency budget: tell us about a system you shipped with a hard response-time limit. How did you design, measure, and defend the budget?
• LLM + model hybrid: how would you combine a trained game model with an LLM explanation layer so the explanation never contradicts the move?
• Hands-on + lead: how do you balance personally coding the hard parts with leading an engineer and fronting the client?