ML/AI Engineer
Role summary
A seed-stage crypto company is seeking an ML/AI Engineer to bridge the gap between ML research and production systems. This role focuses on the end-to-end ML lifecycle, including training pipelines, evaluation, inference services, synthetic data generation, and feedback loops. The ideal candidate has a proven track record of shipping ML models into real products, experience with reinforcement learning (including DPO, GRPO, reward modeling), and expertise in LLM post-training workflows (SFT, preference optimization). The ability to critically evaluate research, build initial versions, and maintain system reliability is essential. Familiarity with Crypto or DeFi is a strong plus.
ML/AI Engineer at a seed-stage team working on one of the hardest problems in crypto - backed by tier 1 VCs.
You are the person who takes ML research and turns it into production systems that work. Training pipelines, eval harnesses, inference services, synthetic data generation, feedback loops. Not a research role. Not a backend integration role. The full path from prototype to deployed system.
Needed:
-> proven ability to ship ML models into real products, not just notebooks and demos
-> reinforcement learning experience: applied RL, DPO, GRPO, reward modeling, preference optimization
-> LLM post-training workflows: SFT, preference datasets, rejection sampling, multi-candidate eval
-> you can read a paper, decide if it's useful, build the first version, and keep it reliable as requirements change
Crypto or DeFi familiarity a strong plus. PST/EST timezone overlap required.
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