ML Engineer
$160,000–$180,000 year
HybridVancouver, British Columbia, Canada
Job Summary
Own the machine learning and applied AI side of the product, turning agent telemetry into detections, risk scores, and behavioral baselines that govern AI agent execution in regulated enterprises. Lead end-to-end modeling work including classification from runtime telemetry, behavioral threat detection, and using LLMs to bridge security intent with machine-enforceable policy. Shape architecture decisions for the telemetry pipeline, product features, model deployment, and A/B testing of detection models in production. This role requires 5+ years of professional ML engineering experience with strong fundamentals in classical machine learning, anomaly detection, and time-series modeling. Based in Vancouver (Hybrid) with a salary range of CAD $160,000 to CAD $180,000, you will report directly to the CTO on a small team focused on shaping early-stage product development.
Required Qualifications
- 5+ years of professional ML engineering experience
- 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
- excellent writing skills
- Vancouver (Hybrid)
Desired Qualifications
- 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
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.