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Salary: CAD 160,000 - 180,000 / annual
Tigera is building a new product to detect, understand, and govern AI agents running in enterprise infrastructure. The company provides Calico, a widely-adopted container networking and security platform used by over 100M containers across 8M+ nodes globally, trusted by companies like Discover, Chipotle, NBCUniversal, and Royal Bank of Canada.
You will own the machine learning and applied AI systems that power this agent detection and governance product. Your responsibilities include:
- End-to-end ownership of ML/AI systems that transform agent telemetry into detections, risk scores, and behavioral baselines
- Building classification models from runtime telemetry, behavioral threat detection systems, and LLM-based solutions to bridge security intent and machine-enforceable policy
- Serving as the AI/ML voice in architecture decisions across the telemetry pipeline, product features, roadmap, model deployment, versioning, and production A/B testing
- Designing ML systems built on top of existing data infrastructure
You bring 5+ years of professional ML engineering experience with at least 2 years deploying production ML systems. You have strong fundamentals in classical machine learning (gradient-boosted trees, regression, classification, feature engineering, handling class imbalance). You've built anomaly detection or time-series models in domains like fraud detection, observability, or fault detection. You have hands-on experience with LLMs for applied tasks—function calling, RAG, prompt engineering, fine-tuning, and evaluation—beyond chatbots.
You're proficient in Python and the standard ML ecosystem (scikit-learn, PyTorch/TensorFlow, pandas) and comfortable working with large-scale telemetry data (ClickHouse, BigQuery, Snowflake, Spark). Strong communication and writing skills are essential.
Nice-to-have skills include security/infrastructure/systems-adjacent ML experience, familiarity with eBPF and kernel telemetry, experience deploying models in latency-sensitive paths, open-source contributions, model versioning frameworks (MLflow, Weights & Biases, BentoML), and interpretable ML background.
The team is small and flat, reporting directly to the CTO. You'll own significant product chunks end-to-end with high autonomy. The company ships fast, reviews architecture openly, and uses Claude Code internally—you're expected to leverage AI tooling effectively and contribute ideas on how AI can make the team more effective.
