Senior AI/ML Engineer

## Responsibilities - Own the machine learning and applied AI side of the product, turning agent telemetry into detections, risk scores, and behavioural baselines. - Design and implement models for classification from runtime telemetry, behavioural threat detection, and using LLMs to bridge security intent and machine-enforceable policy. - Be the AI/ML voice in broader architecture decisions, including telemetry pipeline, product features, model deployment and versioning, and A/B testing of detection models in production. - Work on detecting agents at runtime, understanding their behaviour, distinguishing legitimate activity from misbehaviour, and giving security teams the controls they need without slowing the platform down. ## Requirements - 5+ years of professional ML engineering experience, with at least two years building and deploying production ML systems. - Strong fundamentals in classical machine learning - gradient-boosted trees, regression, classification, evaluation methodology, feature engineering, dealing with class imbalance and noisy labels. - Experience with anomaly detection or time-series modelling in adjacent domains (fraud detection, observability, recommendation systems, fault detection etc). - Hands-on experience using LLMs for applied tasks beyond chatbots - function calling, retrieval-augmented generation, prompt engineering, fine-tuning, evaluation. - Python and the standard ML ecosystem (scikit-learn, PyTorch or TensorFlow, pandas). - Comfort working with large-scale telemetry data - ClickHouse, BigQuery, Snowflake, Spark, or equivalent. - Strong communication skills, including excellent writing skills. ## Nice to Have - Prior experience in security, infrastructure, or systems-adjacent ML. - Familiarity with eBPF, kernel telemetry, or low-level systems observability. - Experience deploying ML models in latency-sensitive paths (sub-millisecond inference). - Open-source contributions to ML tooling or applied AI projects. - Experience with model versioning and various frameworks (e.g. MLflow, Weights & Biases, BentoML, or equivalent). - Background in interpretable ML making model decisions defensible to enterprise customers and auditors. ## Benefits - Opportunity to publish research, speak at conferences (KubeCon, AI Engineer Summit, security research venues), and represent Tigera in the technical community. - Small team, high autonomy. You will own significant chunks of the product end-to-end rather than working through layers of management. - Work with AI assistance tools like Claude Code. - In-office presence twice a week every Monday and Wednesday. - Competitive compensation package.