Beltic verifies agents in real time, on the request path. That means the
models behind it run under the same latency budget as the rest of production
- there is no offline batch job to hide behind, and no second chance if a
call is slow or wrong.
You can work remotely from anywhere in the Americas, between UTC-8 and
UTC-3, or on-site from our San Francisco office - whichever you prefer. We
are not hiring outside that band for this role.
Must-haves
- Years of production ML - building, evaluating, and deploying models, not
just research or notebooks
- Strong Python and a modern ML framework (PyTorch or TensorFlow); solid SQL
- Production 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 labels
from scratch
- Data analysis, statistics, and experiment design
- Strong software engineering - you ship versioned, testable, reproducible
code, not throwaway models
Strongly preferred
- Applied ML in fraud, risk, abuse, account integrity, or security
- Graph ML / GNNs, sequence or behavioral modeling, or anomaly detection
- LLM / NLP work, and familiarity with AI agents - tool use, function
calling, MCP
- Adversarial settings, where the threat evolves against the model
- Explainable, auditable, reproducible ML, with real model versioning
Bonus
- Payment protocols (x402), agent identity (DID / VC), or crypto commitments
(Merkle anchoring)
- Intro call (30 minutes)
- Technical conversation about work you have done (60 minutes)
- Paid take-home or a pairing session, your choice
- Final conversation with the founders
We reply to every application.