Senior Ai / Machine Learning Engineer

Senior AI / Machine Learning Engineer
Location: Remote (Canada)
Engagement Type: B2B contractor
Team: Data Science & AI
Reports to: Head of Data Science & AI
About TieTie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.
At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.
The RoleWe are looking for a Senior AI / Machine Learning Engineer to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of machine learning, graph data, and real-time systems, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.
This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.
What You’ll Do
Design and deploy production-grade ML models for identity resolution, propensity scoring, deliverability, and personalization
Build and maintain feature pipelines across batch and real-time systems (BigQuery, streaming events, graph-derived features)
Develop and optimize classification models (e.G., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
Integrate ML models directly with graph databases to support real-time inference and identity scoring
Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
Partner with engineering to expose models via low-latency APIs and scalable services
Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
Help shape ML best practices, tooling, and standards across the team
What You’ll BringRequired Qualifications
5+ years of experience building and deploying machine learning systems in production
Strong proficiency in Python for ML, data processing, and model serving
Hands-on experience with feature engineering, model training, and evaluation for real-world datasets
Experience deploying ML models via APIs or services (e.G., FastAPI, containers, Kubernetes)
Solid understanding of data modeling, SQL, and analytical workflows
Experience working in a cloud environment (GCP, AWS, or equivalent)
Preferred / Bonus Experience
Experience with graph data, graph databases, or graph-based ML
Familiarity with Neo4j, Cypher, or graph algorithms (community detection, entity resolution)
Experience with XGBoost, tree-based models, or similar classical ML approaches
Exposure to real-time or streaming systems (Kafka, Pub/Sub, event-driven architectures)
Experience with MLOps tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
GPU or large-scale data processing experience (e.G., RAPIDS, CUDA, Spark, or similar)
Domain experience in identity resolution, marketing technology, or email deliverability
Our Technology StackML & Data: Python, Pandas, Scikit-learn, XGBoost
Graphs: Neo4j (Enterprise, GDS)
Cloud: Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
Infrastructure: Docker, Kubernetes, GitHub Actions
APIs: FastAPI, REST-based inference services
What We Offer
Opportunity to work on core AI systems that directly impact revenue and product differentiation
High ownership and autonomy in a senior, hands-on role
Remote-first culture with a strong engineering and data focus
Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems
Clear growth path toward Staff / Principal IC roles
Why This Role MattersAt Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.
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