Tigera provides Calico, a unified network security and observability platform to prevent, detect and mitigate security breaches in Kubernetes clusters. Tigera’s open-source offering, Calico Open Source, is the most widely adopted container networking and security solution.
Powering more than 100M containers across 8M+ nodes in 166 countries, Calico software is supported across all major cloud providers and Kubernetes distributions, and is used by leading companies including Discover, Chipotle, NBCUniversal, HanseMerkur, Box, Siemens Healthineers, Playtech, Royal Bank of Canada, and Bell Canada.
As our team grows, we are looking for colleagues who not only share our passion for this work and growing our company, but who will also strengthen our company values and help ensure that Tigera remains a great place to work. At our core, our focus is on our customers, who are the heroes of our story; on aiming high and staying nimble in how we get there; on continuous learning to drive our success; and on respecting, collaborating, and supporting each other on a daily basis.
If you are looking to help make a substantial impact, and our values and products align with your vision of your career growth, we want to hear from you!
What we're working on AI agents are showing up in enterprise infrastructure faster than platform teams can identify them and security teams can govern them. The technical surface is new - agents make decisions, call tools, hold credentials, and reach across systems in ways traditional models weren't designed for. The companies running thousands of these agents in production have problems nobody has clean answers to yet. Tigera has built a new product to tackle this space. We're a focused engineering team working on the hard parts: 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. The problems span machine learning, distributed systems, applied AI, and security research. We're early enough that the work you do will shape the product, and far enough along that you'll see real customers using what you build.
For this position, we are looking to hire in Vancouver (Hybrid).
Vancouver Salary Range: CAD $160,000 to CAD $180,000
You Will- Own the machine learning and applied AI side of this product, turning the considerable agent telemetry our platform captures into the detections, risk scores, and behavioural baselines that decide whether an AI agent gets to run inside a regulated enterprise. The modelling work is central to what makes the product work, and you'll own it end to end. Including classification from runtime telemetry, behavioural threat detection and using LLMs to bridge the gap between security intent and machine-enforceable policy.
- Be the AI/ML voice in our broader architecture decisions - the telemetry pipeline, product features and roadmap, model deployment and versioning, A/B testing of detection models in production. Our data infrastructure is in place; the ML systems built on top of it are yours to design.
- 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
- 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
Small team, high autonomy. You will own significant chunks of the product end-to-end rather than working through layers of management. We plan and ship fast and review architecture decisions openly. The CTO is the engineering leader and the team is flat below that. You will report directly to the CTO.
We use Claude Code internally and our engineers move quickly with AI assistance, we expect you to as well, and to bring ideas about where AI tooling makes the team more effective.
External visibility can also be part of the role. You will have the opportunity to potentially publish your research, speak at conferences (KubeCon, AI Engineer Summit, security research venues), and represent Tigera in the technical community. This is a real opportunity to build a profile in a fast-growing space.
With offices in San Francisco, San Jose, Vancouver (Canada), Cork (Ireland), and London (England), we have a thriving team of diverse individuals from all over the world. We believe in a collaborative, flexible work environment based on respect for, and commitment from, every employee. We also offer a competitive compensation package along with full health, vision, and dental benefits. These benefits, coupled with an amazing team of individuals who believe in our mission and value openness, collaboration, and teamwork, make Tigera an awesome place to work.
Skills Required
- 5+ years of professional machine learning engineering experience
- At least 2 years building and deploying production machine learning systems
- Strong fundamentals in classical machine learning, including gradient-boosted trees, regression, classification, evaluation, feature engineering, class imbalance, and noisy labels
- Experience with anomaly detection or time-series modeling
- Hands-on experience using LLMs for applied tasks, including function calling, retrieval-augmented generation, prompt engineering, fine-tuning, and evaluation
- Proficiency in Python and the standard machine learning ecosystem, including scikit-learn, PyTorch or TensorFlow, and pandas
- Experience working with large-scale telemetry data using ClickHouse, BigQuery, Snowflake, Spark, or equivalent
- Strong communication skills and excellent writing skills
- Prior experience in security, infrastructure, or systems-adjacent machine learning
- Familiarity with eBPF, kernel telemetry, or low-level systems observability
- Experience deploying machine learning models in latency-sensitive paths, including sub-millisecond inference
- Open-source contributions to machine learning tooling or applied AI projects
- Experience with model versioning frameworks such as MLflow, Weights & Biases, or BentoML
- Background in interpretable machine learning
What We Do
Tigera provides the industry’s only active security platform with full-stack observability for containers and Kubernetes. We are also the creator and maintainer of Calico Open Source, the most widely used container networking and security solution. Calico software powers more than 100M containers across 2M nodes in 166 countries, and is supported across all major cloud providers and Kubernetes distributions.
