ML Engineer

Beltic; United States; Remote; USD 120.000 - 230.000; Vollzeit

Zusammenfassung

Beltic is seeking a seasoned machine learning engineer to design, deploy, and monitor real-time models in production. You will build feature pipelines, handle training and evaluation, and ensure low-latency serving for live applications.

You can work remotely from anywhere in the Americas (UTC-8 to UTC-3) or on-site in San Francisco; candidates with strong Python, PyTorch or TensorFlow, SQL, and software engineering discipline will thrive in a fast-moving environment.

Qualifikationen

• Experience implementing production ML models from data exploration to deployment.
• Strong Python programming and experience with PyTorch or TensorFlow; solid SQL skills.
• End-to-end production ML systems: feature pipelines, training, evaluation, and low-latency serving.
• Comfort with low-label and cold-start problems such as anomaly detection and weak supervision.
• Data analysis, statistics, and principled experiment design.
• Strong software engineering: versioned, testable, reproducible code.

Kenntnisse

Production ML

Python

PyTorch or TensorFlow

SQL

Low-latency serving

Experiment design

Software engineering

Anomaly detection

Ausbildung

Bachelor's in CS/Math/Related

Tools

Git

Docker

Jobbeschreibung

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.

What we are looking for

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)

How we hire

1. Intro call (30 minutes)
2. Technical conversation about work you have done (60 minutes)
3. Paid take-home or a pairing session, your choice
4. Final conversation with the founders

We reply to every application.