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Founding Member of Technical Staff — Machine Learning

Vectoral United States

Sep 16
Remote machine-learning Senior (5-10 yrs) Full-time United States
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About the role

You will own the end-to-end detection logic, including signals, rules, and trained models used to identify fraud and abuse in LLM API traffic. You will also build and maintain the training pipelines, model-replay workers, and adversarial red-team harnesses to ensure robust security.

What they look for

Machine learning Go Python LightGBM Psycopg Scikit-learn Fraud detection Anti-abuse systems Security engineering Data modeling Adversarial testing Precision/recall optimization Gradient boosting System architecture API security

Requirements

The ideal candidate is an experienced applied ML or anti-abuse practitioner with a strong background in shipping production-grade detection systems. You must be proficient in Python and Go, with deep expertise in gradient boosting, calibration, and handling class imbalance.

Benefits

Competitive salary Equity Health insurance Dental insurance Vision insurance Unlimited paid time off Flexible work hours

Full description

Founding Member of Technical Staff — Machine Learning

 

About Vectoral

Vectoral is a B2B security company protecting LLM API traffic from fraud and abuse, including token resellers, account farms, denial-of-wallet attacks, automated scraping, CSAM, and model distillation.

We’re already working with some of the largest names in inference and are looking for exceptional engineers to help us build and scale the security layer for AI infrastructure.

 

Role Overview

Own whether we catch the fraud. Everything that goes into making a verdict. The signals, rules, trained models, thresholds, and evidence that it works.

 

What You’ll Work On

  • The detection algorithm itself: a rule-and-group engine in Go that produces every live verdict. Signal groups like machine-paced behavior, birth cohort, probing, resource shape, value extraction, datacenter origin, and account risk. Each with activation logic, per-customer presets, calibration, and cold-start handling for accounts we've barely seen.
  • A LightGBM training pipeline (Python, psycopg, scikit-learn) that pulls labels and feature snapshots per customer, fits a binary classifier, Platt-calibrates on a temporally held-out split, and writes a candidate model.
  • A model-replay worker that scores stored snapshots offline, allowing new changes to be evaluated against real historical traffic before affecting live verdicts.
  • An adversarial red-team harness where a blind LLM adversary plays an abuser attacking a customer's product.

 

What We Offer

 

We want to build an exceptional team, and we're willing to go above and beyond to get the right people.

 

  • Competitive salary and equity
  • Health, dental, and vision insurance
  • Unlimited paid time off
  • Flexible work hours

 

Who We’re Looking For

Someone who is an applied ML or detection/anti-abuse practitioner. They ship the model and the code that runs it in production, on a latency-sensitive path

  • Built fraud, abuse, anti-bot, risk, or security detection systems that operated against live adversaries, and can discuss how these systems were evaded and the actions taken in response
  • Fluent in gradient boosting, calibration, temporal train/test splitting, and precision/recall tradeoffs under severe class imbalance; understands why AUC alone is insufficient to argue a model is ready
  • Strong Python; can read and write Go, or will get there fast.

Why Join Us

  • Build from day one on a product making a real impact with active customers.
  • Founding engineering role: shape product, architecture, and company culture from the ground up.
  • Significant technical ownership and direct influence over what we build and how we build it.
  • Work with a tight, highly skilled team on problems where robust systems matter and where your work directly contributes to customer security and satisfaction.

 

If you’re excited to build products that stand up to adversaries, enjoy shipping code that matters, and want to join an early team where your impact is outsized, we want to talk.

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