Senior Machine Learning Engineer

About the Position

Join a team where empathy meets engineering - and where your impact truly matters. 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.

Responsibilities

• 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.

Requirements

• 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.

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).

About the Company

Join a team where empathy meets engineering - and where your impact truly matters. We’re committed to creating a diverse environment and are proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.