ML Engineer – Threat Detection Models

📋 Description

• Design, train, and evaluate threat detectors using classifiers, embedding-based models, fine-tuned
• Build and maintain training and evaluation datasets, labeling workflows, and benchmark suites.
• Track model performance through precision/recall, error analysis, drift, and adversarial robustness.
• Serve models in production under strict latency requirements, working in Go to build inference
• Optimize inference for performance and cost through techniques such as quantization, distillation
• Work closely with security researchers to turn emerging attack techniques into training data and

🎯 Requirements

• 4+ years of experience building and deploying ML models in production, including NLP or LLM-based
• Strong Python skills and experience with the modern ML stack, including PyTorch, Hugging Face
• Hands-on experience fine-tuning transformer-based models.
• Solid Go skills, or strong backend engineering experience with the ability to become productive in
• Experience with low-latency model serving, using technologies such as ONNX Runtime, TorchServe
• Strong evaluation discipline, including dataset design, metrics, error analysis, and adversarial

🎁 Benefits

• Build real-time AI threat detection systems at the intersection of cybersecurity and agentic AI.
• Take models from research and experimentation all the way into low-latency production environments.
• Work on challenging problems spanning ML, LLM security, adversarial AI, and distributed systems.
• Own a critical detection workstream and influence how AI security threats are identified and
• Collaborate with a highly technical, distributed team where AI is a core part of the development