The Role
Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.
Summary Generated by Built In
Senior AI / Machine Learning Engineer
Location: Remote (US)
Employment Type: Full-time, W-2
Benefits: Health, vision, and dental insurance + equity options
Compensation: $135,000 USD/year
Team: Data Science & AI
Reports to: Head of Data Science & AI
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.
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
- 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)
- 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
- 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
- Competitive compensation, including salary, equity, and performance incentives
- 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
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.
Skills Required
- 5+ years of experience building and deploying machine learning systems in production
- Strong proficiency in Python for machine learning, data processing, and model serving
- Hands-on experience with feature engineering, model training, and evaluation for real-world datasets
- Experience deploying machine learning models through APIs or services
- Solid understanding of data modeling, SQL, and analytical workflows
- Experience working in a cloud environment such as GCP or AWS
- Experience with graph data, graph databases, or graph-based machine learning
- Familiarity with Neo4j, Cypher, or graph algorithms
- Experience with XGBoost, tree-based models, or similar classical machine learning approaches
- Exposure to real-time or streaming systems such as Kafka or Pub/Sub
- Experience with MLOps tooling and practices
- GPU or large-scale data processing experience
- Domain experience in identity resolution, marketing technology, or email deliverability
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The Company
What We Do
Tie is an AI-powered shopper-intelligence platform for ecommerce brands. It helps marketing teams identify anonymous website visitors, connect shopper identities across devices and channels, enrich profiles with demographic and intent data, and predict purchase and engagement likelihood. Tie also optimizes email inbox placement and sends actionable audiences into existing marketing systems, helping brands personalize outreach, improve customer acquisition and retention, and earn more from web traffic.
