Machine Learning Engineer

Iterable, Inc.; Lisboa; Vor Ort; EUR 67.000 - 88.000; Vollzeit

Benefits dieser Stelle

Private Medical Insurance

Meal Allowance: 8.55€ per day

Community Days

Paid Annual Leave (22 days)

Global Lifestyle Reimbursement Account

Zusammenfassung

Iterable, Inc. is hiring a Senior Machine Learning Engineer in Lisbon to design and build end-to-end ML systems powering our product. You will own the models from research to production, enabling personalized customer experiences for thousands of enterprise clients.

You will work with Databricks, Spark, AWS, Ray, and Kubernetes to build scalable pipelines, improve feature serving, and set engineering standards. A growth mindset and hands-on leadership are highly valued at Iterable.

Qualifikationen

• 5+ years of hands-on ML engineering in production.
• Experience with large-scale recommendation systems and personalization.
• Proficiency in Python and/or Scala for production-quality code.
• Demonstrated ability to build/maintain data pipelines and model serving in production.
• Exposure to Infrastructure-as-Code (Terraform, Pulumi) is a plus.

Aufgaben

• Build and ship core product ML capabilities, including send-time optimization, recommendation engines, and campaign personalization.
• Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, deployment, monitoring, and online experimentation.
• Build and scale data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
• Collaborate with Backend and Platform teams to improve data architecture and establish engineering standards.
• Mentor engineers and help set the technical direction for ML at Iterable.

Kenntnisse

MLE experience

Python/Scala

Model deployment

Systems & Infra

Data pipelines

Recommendation systems

Tools

Databricks

Spark

AWS

Ray

Kubernetes

Jobbeschreibung

Iterable is the leading AI-powered customer engagement platform that helps leading brands like Redfin, SeatGeek, Priceline, Calm, and Box create dynamic, individualized experiences at scale. Our platform empowers organizations to activate customer data, design seamless cross-channel interactions, and optimize engagement—all with enterprise-grade security and compliance. Today, nearly 1,200 brands across 50+ countries rely on Iterable to drive growth, deepen customer relationships, and deliver joyful customer experiences.

As a Senior Machine Learning Engineer at Iterable, you will design and build the end-to-end machine learning systems that power intelligence across our product. As a high-tech marketing platform, we work with thousands of large-scale and diverse enterprise customers. The scale and diversity of these workloads — supporting personalization with deep customizability — present unique and exciting challenges for machine learning at production scale.

You will have real autonomy: from research and experimentation through to models running reliably in production, you'll own the systems and models behind our ML products and help shape the direction of AI at Iterable.

One of our core values is a growth mindset, and Iterable is a company where everyone can grow.

What You'll Do

• Build and ship core product ML capabilities, including send-time/frequency optimization, recommendation engines, and campaign personalization.
• Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., multi-armed bandits, ensemble models).
• Build and scale our data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
• Partner with Backend and Platform teams to elevate feature serving, improve overall data architecture, and establish engineering standards across the team.
• Mentor engineers on the team and help set the technical direction for ML at Iterable.

What We're Looking For

• 5+ years of hands-on MLE experience putting complex models into production at scale.
• Databricks & Spark mastery: Real experience building, optimizing, and scaling production data pipelines using Databricks.
• Production-level Model Engineering: Proven track record of taking deep learning and ensemble architectures from training through optimization to production deployment.
• Production Code: Strong Python and/or Scala skills with an emphasis on readable, modular, and maintainable systems code.
• Systems & Infra Experience: Hands-on work with microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes).
• Pragmatism: You care more about serving latency, reliability, and measurable customer impact than novel theoretical architectures.
• Prior work on large-scale recommendation systems.
• Experience setting up feature stores or model observability tools.
• Exposure to Infrastructure-as-Code (Terraform, Pulumi).

You Might Work On

• Building, evaluating, and integrating new models that add intelligence into our core product.
• Enhancing models that optimize send time and frequency of outgoing message campaigns.
• Designing data pipelines that make it easy to implement and test new models.
• Working with platform engineers to enhance our feature- and model-serving architecture.
• Benchmarking and improving our multi-armed bandit implementation.
• Building tools to visualize and debug features and models.

What we offer

• Private Medical Insurance
• Meal Allowance: 8.55€ per day
• Community Days (days for us to give back to the community)
• Paid Annual Leave (22 days)
• Global Lifestyle Reimbursement Account

The base salary range for this position in Lisbon, Portugal, is €66,500 – €88,000 per year (gross). This range is provided in anticipation of the requirements of the EU Pay Transparency Directive (2023/970), which EU member states are implementing into national law, and reflects the expected compensation at the start of employment for this role. The final offer will be determined based on your experience, qualifications, and performance throughout the selection process, while also ensuring alignment with our internal equity principles.