Member of Technical Staff Machine Learning & Agent Security Engineering

What you'll do

• Develop and operate high-availability production AI and LLM agentic systems using Python and tool calling.
• Build scalable data-processing pipelines and automated security intelligence systems for large-scale telemetry.
• Operationalize machine learning models for classification, clustering, and anomaly detection to automate threat detection.
• Take end-to-end ownership of software lifecycle including implementation, testing, deployment, and live production operations.
• Ensure system resilience and performance through robust telemetry, logs, metrics, and traces.
• Translate emerging agentic AI patterns into reliable, well-tested software services and APIs.
• Solve complex implementation challenges related to scale, concurrency, and high-volume data processing.

What we're looking for

• 3+ years of professional software engineering, machine learning engineering, or related development experience.
• Strong hands-on programming skills in Python.
• Solid software engineering fundamentals including data structures, APIs, testing, debugging, and maintainable design.
• Experience building and operating production software, services, data-processing systems, or ML applications.
• Experience solving implementation challenges involving scale, performance, reliability, data volume, or concurrency.
• Practical understanding of machine learning fundamentals and experience applying ML using common libraries or frameworks.
• Familiarity with Generative AI and LLM technologies integrated into software applications.
• Understanding of software development practices including source control, automated testing, CI/CD, monitoring, and operational debugging.
• Experience working with cloud-based, distributed, or data-intensive applications.
• Ability to troubleshoot software using logs, metrics, traces, and other telemetry.
• Experience with Agentic AI workflows, tool calling, and state management (preferred).
• Experience with ML frameworks like PyTorch, scikit-learn, Hugging Face, or XGBoost (preferred).
• Experience with distributed/data processing technologies like Ray, Spark, Kafka, or Airflow (preferred).
• Experience with Docker, Kubernetes, or cloud-based ML infrastructure (preferred).
• Experience in MLOps including deploying and monitoring ML models (preferred).
• Familiarity with cybersecurity concepts, threat detection, or security frameworks like MITRE ATT&CK (preferred).
• Exposure to adversarial AI, red teaming, or automated security evaluation (preferred).