​Senior Machine Learning Engineer – Production Systems & Real‑Time Intelligence

We’re seeking an experienced ML engineer to help build the next generation of intelligent, high‑throughput services that power our core identity and scoring ecosystem.

In this role, you’ll work across the full lifecycle of machine learning—data preparation, model development, deployment, optimization, and long‑term reliability. You’ll design systems that operate at extremely low latency, integrate with graph‑based data structures, and run in real production environments at scale.

This role is ideal for someone who loves hands-on engineering, thrives in high‑impact environments, and wants to shape how ML is built, deployed, and operationalized across the company.

What You'll Work On

• Develop and ship ML models used for entity matching, behavioral scoring, deliverability prediction, and personalization.

• Build robust feature pipelines for both scheduled and streaming workflows, including transformations sourced from graph networks, analytics stores, and real-time event feeds.

• Train, tune, and evaluate classification models—including handling skewed datasets, messy labels, and complex data patterns.

• Integrate models with graph technologies to support real-time identity inference and relationship-aware predictions.

• Own the operational lifecycle of deployed models: monitoring, quality checks, drift alerts, retraining triggers, and performance dashboards.

• Collaborate with platform and backend teams to expose models through low-latency services and scalable APIs.

• Contribute to GPU-enabled workloads and high-volume computation pipelines that dramatically reduce processing times.

• Help define team-wide ML engineering practices, tooling, and architectural patterns.

What You Bring

Required

• 5+ years building and running ML systems in production environments.

• Strong experience with Python for data, modeling, and scalable model serving.

• A track record of hands-on work with feature engineering, training pipelines, and real‑world evaluation.

• Experience deploying models via APIs or containerized services (e.g., FastAPI, Docker, Kubernetes).

• Comfortable working with SQL, data modeling, and analytical workflows.

• Familiarity with at least one major cloud provider (GCP, AWS, etc.).

Nice to Have

• Exposure to graph technologies or graph‑driven machine learning.

• Experience with Neo4j, Cypher, or graph algorithms for clustering, ranking, or entity resolution.

• Practical knowledge of tree‑based ML approaches (XGBoost or similar).

• Background in streaming or event-driven architectures (Kafka, Pub/Sub).

• MLOps experience: automated training, monitoring, CI/CD for ML systems.

• Experience with GPU acceleration or large-scale computation frameworks.

• Domain familiarity with identity, marketing analytics, or deliverability systems.

What You’ll Get

• Competitive compensation—including salary, equity, and performance-based incentives.

• A chance to work on foundational AI systems that directly influence product performance and revenue.

• High ownership in a role where you shape key architectural decisions.

• A remote-first culture centered around engineering, experimentation, and autonomy.

• Opportunities to work on advanced problems in real‑time inference, graph ML, and identity intelligence.

• Clear pathways to Staff and Principal IC growth.

If this position is of interest to you, apply now!