
51 - 200 employees
🤖 Artificial Intelligence
🏥 Healthcare
⚡ Energy
💰 Corporate Round on 2022-10
Artificial Intelligence • Healthcare • Energy
Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries.
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51 - 200 employees
🤖 Artificial Intelligence
🏥 Healthcare
⚡ Energy
💰 Corporate Round on 2022-10
Artificial Intelligence • Healthcare • Energy
Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries.
• Design and build the mahjong-playing algorithm using imitation learning, reinforcement learning, search with MCTS, or a hybrid approach • Train, evaluate, and ship the mahjong-playing model • Own the end-to-end technical architecture, including the game model, LLM reasoning layer, valid-action mask, win detection, and API contract with the game bridge • Design and measure the inference path, batching, and caching to meet the 2-second response budget • Define required client data and event names, including hand histories and event streams • Build the training and calibration pipeline using client data • Build and run an evaluation harness for play strength and explanation quality • Stand up LLM observability with Langfuse using async logging and N+1 batch • Take over context from Vlad Borysenko during ramp-up and lead sprint work with the AI Engineer • Work with the client's Product Owner in a scrum process • Present technical decisions and trade-offs to the client's CTO and engineers • Monitor risks involving licensing of new training data, engine-bridge capabilities, and multi-rule-set scope
• 6+ years hands-on ML/AI engineering experience, with real game AI or sequential decision-making work • Hands-on RL, imitation learning, or search-based agents such as MCTS and self-play • Experience with imperfect-information games ideally including mahjong, poker, or card games • Expert Python for ML systems • Strong software engineering skills, including APIs, testing, and CI • End-to-end model training on user or gameplay data: data → training → evaluation → serving • LLM application engineering, including reasoning layers, prompt and context design, structured outputs, and guardrails • Low-latency inference experience, including profiling, batching, caching, and model-size trade-offs • LLM observability and evaluation experience with Langfuse or similar • AWS deployment for ML workloads • Game theory for imperfect-information games • Evaluation of play strength using win rates, Elo-style ratings, and baseline agents • Game-engine integration patterns, including event streams, action masks, and state bridges • AWS Well-Architected knowledge for ML workloads • Hands-on ML/AI engineering at production scale • Experience shipping an AI system inside a live product with hard latency limits • Cloud hyperscaler experience, preferably AWS • Technology consulting or client-facing delivery background • Experience training models on gameplay data end-to-end • Experience leading small delivery teams while coding personally • Ability to own an architecture in front of a technical client CTO • Advanced English level required • Availability for part-time remote work • Openness to B2B contractor collaboration
• Part-time, 0.5 FTE engagement for 3 months • Remote work • B2B (contractor) form of collaboration
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