ML Engineer

Beltic; San Francisco (CA); Hybrid; USD 170,000 - 230,000; Full time

Job summary

Beltic is seeking an experienced ML engineer to work on production-scale models with real-time serving requirements. You will build, evaluate, and deploy models using Python and modern ML frameworks, implement robust data pipelines, and design experiments to validate performance.

The role accommodates remote work from the Americas or on-site in San Francisco, with an emphasis on reliable, scalable ML systems and reproducible results.

Qualifications

• Years of production ML — building, evaluating, and deploying models.
• 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.
• Data analysis, statistics, and experiment design.
• Strong software engineering — you ship versioned, testable, reproducible code, not throwaway models.

Skills

Production ML

Python

Experiment design

SQL

Software engineering

Tools

PyTorch

TensorFlow

Job description

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 production

• there 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 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 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 detection
• LLM / NLP work, and familiarity with AI agents - tool use, functioncalling, 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)
• Technical conversation about work you have done (60 minutes)
• Paid take-home or a pairing session, your choice