Senior Machine Learning Engineer

We’re an early-stage startup building next-generation personalization technology for digital marketing. Our mission is to help brands deliver smarter, more relevant customer experiences using data and machine learning.

Role Overview

We’re looking for a Senior Machine Learning Engineer to design and scale production ML systems that power real-time personalization and decision-making at large scale. 

You’ll own the end-to-end ML lifecycle; from transforming raw behavioral data into features, to deploying APIs that deliver predictions in milliseconds, to building the observability needed to keep models trustworthy in production. 

This role requires strong applied ML skills, MLOps expertise, and an ability to think in terms of systems, not just models.

What You’ll Do

• Build and productionize ML models for ranking, personalization, and customer engagement.
• Develop pipelines that turn behavioral, demographic, and contextual signals into online/offline features.
• Design and deploy low-latency APIs and decision services for decision making.
• Implement experimentation frameworks, including A/B testing and exploration exploitation strategies.
• Operationalize the ML lifecycle: automated training, CI/CD for models, artifact and feature versioning, online/offline parity.
• Build observability into ML systems: monitor data quality, model drift, and decision outcomes; trigger retraining where needed.
• Establish closed feedback loops that connect decisions to outcomes (conversions, engagement, fatigue signals like unsubscribes).
• Collaborate with product and engineering to balance personalization, compliance, and business value in real-world systems.
   

What You Bring

• 5+ years experience in applied ML engineering (recommendation systems, personalization, ranking, or ads).
• Strong background in Python/Go, SQL, and modern ML frameworks (TensorFlow, PyTorch, or similar).
• Strong grasp of MLOps practices: CI/CD for ML, containerization (Docker), orchestration (Kubernetes, Airflow/Kubeflow), model registries, monitoring frameworks.
• Familiarity with cloud ML platforms (Vertex AI, SageMaker, or similar) and data warehouses (BigQuery, Snowflake, Redshift).
• Experience deploying real-time ML systems (low-latency serving, feature stores, event-driven architectures).
• Understanding of multi-objective optimization and trade-offs in personalization.

• Comfortable working cross-functionally in a startup environment.

   

Nice to Have

• Experience in martech, adtech, CRM, or large-scale personalization platforms.
• Exposure to bandit algorithms, reinforcement learning, or causal inference for adaptive decision-making.
• Prior work on systems serving millions of users at scale.
• Experience with Google Cloud Platform.
• Experience with observability tools (Prometheus, Grafana, Evidently, WhyLabs, Great Expectations) for monitoring data and model health.