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 Tie

Tie 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 Role

We 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 Bring

Required 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 Stack

ML & 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 Matters

At 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.