ML Engineer

Tigera•Vancouver, BC

10h•CA$160,000 - CA$180,000•Hybrid

About The Position

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 building 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).

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 Haves

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

Responsibilities

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.
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.
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.