Machine Learning Engineer

Description:

We're looking for an exceptional Machine Learning Engineer to build the models that distinguish a trusted AI agent from an attack, in real time and under load.

This is an opportunity to join a small, highly technical founding-stage engineering team and take meaningful ownership of mission-critical ML infrastructure.

What You'll Be Working On

The models behind this platform operate directly on the production request path. They must evaluate activity and support trust decisions in real time, under the same strict latency constraints as the rest of the infrastructure.

There is no offline batch job to hide behind and no second chance if a prediction is too slow or incorrect.

You'll work on problems involving:

• Real-Time Fraud & Threat Detection: Develop ML models that identify potentially fraudulent, malicious, or unauthorized agent activity while minimizing false positives.
• Behavioral & Anomaly Detection: Identify suspicious patterns, anomalous agent interactions, and emerging risks across automated transactions and API activity.
• Low-Latency ML Inference: Build and optimize production model-serving systems capable of making accurate decisions under demanding latency and throughput requirements.
• Cold-Start & Low-Label Learning: Develop effective detection approaches when labeled examples are scarce, including unsupervised learning, weak supervision, and custom evaluation datasets.
• End-to-End ML Ownership: Build, evaluate, deploy, monitor, and continuously improve production ML systems, from data and feature pipelines through real-time serving.
• Adversarial ML: Help build systems that remain effective as malicious actors adapt their behavior to evade detection.
• Agentic AI Infrastructure: Work at the intersection of modern ML, AI agents, tool calling, identity verification, and security.

What We're Looking For

Must-Haves

• 3+ years of professional ML experience, ideally 5+ years, building, evaluating, and deploying production 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, including anomaly detection, unsupervised methods, weak supervision, and building evaluation sets and labels from scratch.
• Data analysis, statistics, and experiment design.
• Strong software engineering. You ship versioned, testable, reproducible code, not throwaway models.
• Experience working in highly technical engineering environments with strong software engineering standards.
• Ability to take ownership, solve ambiguous problems, and ship production-grade solutions in a fast-moving startup environment.

Strongly Preferred

• Applied ML in fraud, risk, abuse, account integrity, or security.
• Experience building ML models for fraud prevention, threat detection, trust and safety, or other systems designed to prevent malicious activity.
• Graph ML / GNNs, sequence or behavioral modeling, or anomaly detection.
• LLM / NLP work, and familiarity with AI agents, including tool use, function calling, and MCP.
• Experience in adversarial settings, where the threat evolves against the model.
• Explainable, auditable, reproducible ML, with real model versioning.
• Experience developing ML systems at highly technical, engineering-driven companies, particularly those working on large-scale infrastructure, payments, fraud prevention, cybersecurity, or AI platforms.