Senior Machine Learning Product Engineer, Communications, Growth Alliance (all genders)

About the role: What's in the Box

The Communications tribe, part of the Growth Alliance, enables personalized, meaningful interactions with users across global markets. As a Senior Machine Learning Engineer, you will shape the intelligence behind how, when, and what is communicated to customers.

Your work will focus on three core initiatives:

• Send Decisioning: Improve the data layer and integrations for the decisioning engine, using the existing feature store and feature sets to optimize email, SMS, and push-notification channels. Models will automate the logic that determines the most relevant message and timing for each user.
• Intelligent Funnel: Support a cross-tribe initiative to personalize the post-click experience and streamline content generation, reducing manual work in email marketing-copy creation.
• AI-Assisted Content & Search: Develop automated, context-based image retrieval for email creation and tools that help marketing teams automatically compose high-performing communication assets.

Success requires translating complex user behavior into scalable models, improving data foundations, and balancing technical depth with a focus on user engagement. The role is aligned to a specific Alliance, but candidates may also be considered for opportunities across different teams or projects.

What you'll do: The Recipe

Product Engineers own customer problems: they form a point of view, validate it with customers and data, and ship solutions using AI as a force multiplier.

• Build, maintain, and refine core repositories, developer tooling, and engineering workflows for scalable production ML systems.
• Build and operate data products and machine-learning systems from experimentation through production, owning real-world performance.
• Evaluate, optimize, and potentially redesign communication-platform integrations; build robust pipelines and integrate the feature store to improve data quality and feature availability for decisioning models.
• Develop scalable integrations and workflows for email copy generation, including subject lines and copy context, and context-driven asset-retrieval systems for marketing teams.
• Build low-latency, highly reliable production pipelines and serving layers with observability, data reliability, and error handling.
• Partner with Data Scientists to transition experimental models into resilient, observable production services with fallback mechanisms.
• Standardize and maintain repositories, CI/CD templates, and ML platform practices using Databricks and MLflow.
• Take accountability for production reliability, including monitoring input-data quality, drift, and latency across communication and personalization funnels.
• Work beyond your specialization when needed and operate, instrument, monitor, and improve systems in production.

What you'll bring: The Ingredients

• Hands-on experience with AI tooling such as Claude Code, Cursor, or Copilot beyond casual experimentation; daily use of AI agents and practical understanding of how context and boundaries affect AI-generated output.
• Deep data/ML engineering expertise, experience operating production models or data products, and statistical literacy to design sound experiments and interpret results.
• Ideally, 5+ years building and operating production ML systems.
• Fluency across Python, Spark, and Databricks, plus working knowledge of Go, Kafka, and Kubernetes.
• Hands-on experience with pipelines, model serving, and observability at scale.
• Statistical literacy to design honest experiments and accurately evaluate model metrics.
• Operational judgment to diagnose system behavior under real load, identify root causes, and deploy robust fixes.
• Full ownership, a bias to ship, product sense, and the ability to support decisions with data and user evidence.

What we offer: The Toppings

• Global collaboration with experienced engineers and product partners across international teams, with active knowledge sharing.
• Opportunity to build and operate modern systems at global scale, supporting more than 6 million customers and complex supply-chain operations.
• Technical, product, and design leadership opportunities to influence architecture, quality, and ways of working in an autonomous, product-led environment.
• End-to-end ownership from problem definition through production, improving systems and enabling long-term scalability.
• Access to a Warsaw-centre workspace at Prosta 20 with modern facilities, including showers, breakout zones, outdoor space, cycle parking, and refreshments.