ServiceNow · Posted 2d ago
Senior Machine Learning Engineer
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇨🇦 Toronto, Canada
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
The posting
Join ServiceNow as a Senior Machine Learning Engineer, where you will design, build, and operate model-driven applications. You will be responsible for developing optimization algorithms, managing deployments, and collaborating with cross-functional teams. This role requires strong experience in software engineering, AI integration, and stakeholder communication.
- Concevoir, construire et exploiter des applications basées sur des modèles d'optimisation avancés en production à grande échelle. - Développer des systèmes d'évaluation automatisés et de test du comportement des modèles, et gérer les déploiements. - Collaborer avec les clients pour comprendre les exigences commerciales et les capacités du produit, et aider à la mise en valeur.
- Experience with key Technologies: Experience with Python is strongly preferred; experience in Java, JavaScript, Kubernetes, or GraphQL is a bonus - Domain expertise. Familiarity with Operations Research, Vehicle Routing Problems, Mathematical Optimization, Field Service Management is a plus but not required - Experience with mission critical systems. Demonstrated experience developing reliable and resilient systems - AI Native Approach. Strong critical thinking and a track record of integrating AI into engineering processes, SDLC, workflows, or decision making - 5+ years of related experience with a bachelor’s degree in computer science, software engineering, or related technical field. Advanced degrees or certifications (especially in Operations Research) are a bonus - Software engineering fundamentals. Strong command of OOP, data structures, algorithms, system design, concurrency, parallelism, APIs, data modeling, and testing - Forward deployed delivery and stakeholder communication. Comfort working directly with customers under incomplete information and fixed timelines, judgment about when a fast local solution is right and when to hold out for the durable one, and credibility communicating capability, limitation, and risk to non-engineers - Python, MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL