ML Engineer

Beltic verifies agents in real time, on the request path. That means themodels behind it run under the same latency budget as the rest of productionthere is no offline batch job to hide behind, and no second chance if acall is slow or wrong. You can work remotely from anywhere in the Americas, between UTC-8 andUTC-3, or on-site from our San Francisco office - whichever you prefer. Weare not hiring outside that band for this role. Years of production ML - building, evaluating, and deploying models, notjust research or notebooks Strong Python and a modern ML framework (PyTorch or TensorFlow); solid SQLProduction ML systems end to end: feature pipelines, training, evaluation, and low-latency, real-time serving Comfort with low-label and cold-start problems - anomaly detection, unsupervised methods, weak supervision, and building eval sets and labelsfrom scratch Data analysis, statistics, and experiment design Strong software engineering - you ship versioned, testable, reproduciblecode, not throwaway models Strongly preferred Applied ML in fraud, risk, abuse, account integrity, or security Graph ML / GNNs, sequence or behavioral modeling, or anomaly detectionLLM / NLP work, and familiarity with AI agents - tool use, functioncalling, MCPAdversarial settings, where the threat evolves against the model Explainable, auditable, reproducible ML, with real model versioning BonusPayment protocols (x402), agent identity (DID / VC), or crypto commitments(Merkle anchoring) Technical conversation about work you have done (60 minutes) Paid take-home or a pairing session, your choice