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 scale.
In this role, you’ll own the full ML lifecycle—from transforming raw behavioral data into meaningful features, to deploying low-latency prediction APIs, to building the observability needed to keep models reliable in production.
This is a great opportunity for someone with strong applied ML and MLOps expertise who enjoys solving complex engineering challenges and building scalable, high-impact systems. Your responsibilities will include: * Build and productionize ML models for ranking, personalization, and customer engagement. * Develop pipelines that transform behavioral, demographic, and contextual signals into online and offline features. * Design and deploy low-latency APIs and decision services for real-time 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, and online/offline parity. * Build observability into ML systems by monitoring data quality, model drift, and decision outcomes, and triggering retraining when needed. * Establish closed feedback loops that connect decisions to business outcomes (e.g. conversions, engagement, fatigue signals such as unsubscribes). * Collaborate closely with product and engineering teams to balance personalization, compliance, and business value in real-world systems.
What we expect from you: * 5+ years of experience in applied ML engineering (recommendation systems, personalization, ranking, or advertising systems). * Strong proficiency in Python or Go, SQL, and modern ML frameworks such as TensorFlow, PyTorch, or similar. * Strong understanding of MLOps best practices, including CI/CD for ML, containerization (Docker), orchestration (Kubernetes, Airflow, Kubeflow), model registries, and monitoring frameworks. * Familiarity with cloud ML platforms such as Vertex AI, SageMaker, or similar, and data warehouses like BigQuery, Snowflake, or Redshift. * Experience deploying real-time ML systems, including low-latency serving, feature stores, and event-driven architectures. * Understanding of multi-objective optimization and trade-offs in personalization systems. * Comfort working cross-functionally in a dynamic startup environment with the overlap within USA time zone. * Strong spoken and written English communication skills.
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. * Experience building systems serving millions of users at scale. * Hands-on experience with Google Cloud Platform (GCP). * Familiarity with observability tools such as Prometheus, Grafana, Evidently, WhyLabs, or Great Expectations for monitoring data and model health.
What we offer: * Interesting projects and technical challenges that support both professional and personal growth. * A long-term project with stability and impact. * A flexible, results-oriented schedule with hybrid or remote work options. * A comfortable, modern office in Kyiv with generator and battery backup. * Competitive salary, medical insurance, and a supportive onboarding/trial period. * Team-building events, including parties, online activities, picnics, and more. * The opportunity to work in a Top Employer company (DOU 2025).
