ML Engineer – Threat Detection Models

This is a fully remote position, open to applicants in India.

📋 Description

• Design, train, and assess threat detectors using classifiers, embedding-based models, fine-tuned LLMs, and hybrid rule/ML strategies.

• Construct and sustain training and evaluation datasets, labeling workflows, and benchmark suites.

• Monitor model performance through precision/recall, error analysis, drift, and adversarial robustness.

• Deploy models in production with stringent latency requirements.

• Develop inference services in Go and ensure integration with gateway and endpoint pipelines.

• Enhance inference performance and reduce costs through quantization, distillation, batching, and caching techniques.

• Collaborate with security researchers to transform emerging attack methods into training data and detection frameworks.

• Establish and maintain MLOps workflows for reproducible training, model registries, monitoring, and safe deployment of models.

• Leverage AI-assisted development workflows for implementation, testing, debugging, and experimentation.

⛳️ Requirements

• Over 4 years of experience in building and deploying ML models in production, particularly in NLP or LLM-based classification.

• Proficient in Python.

• Familiarity with PyTorch, Hugging Face Transformers, and scikit-learn.

• Practical experience in fine-tuning transformer-based models.

• Solid skills in Go, or substantial backend engineering experience with the ability to quickly become proficient in Go.

• Experience in low-latency model serving utilizing ONNX Runtime, TorchServe, Triton, vLLM, or custom serving infrastructure.

• Strong evaluation discipline, encompassing dataset design, metrics, error analysis, and adversarial testing.

• Proficient in Docker and Kubernetes.

• Experience with at least one major cloud service provider.

• Fluent in English with excellent written and verbal communication skills.

• Comfortable using AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex, or similar tools; this is essential.

• Demonstrated ownership and ability to work independently in a remote-first, distributed environment.

🏝️ Benefits

• Remote-first work arrangement.

• Opportunity to collaborate with a global enterprise cybersecurity organization.

• Engage in real-time AI threat detection systems.

• Responsibility for a vital detection workstream.

• Work alongside a highly skilled, distributed team.

• Utilize AI-assisted development workflows.

• Inclusive and transparent recruitment process.