Senior Machine Learning Engineer

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.

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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.
• 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).

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