Senior Machine Learning Engineer

iterable

Hybrid - Lisbon, Portugal · Hybrid - Lisbon · Portugal

Posted Aug 4, 2026

Contract
Remote
Engineering

Job description

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.

Our success is powered by extraordinary people who bring our core values—Be an Owner, Growth Mindset, Run as One, Transparency —to life. We foster a culture of innovation, collaboration, and inclusion, where ideas are valued and individuals are empowered to do their best work. That’s why we’ve been recognized as one of Inc’s Best Workplaces and Fastest Growing Companies, and were recognized on Forbes’ list of America’s Best Startup Employers in 2022. Notably, Iterable has also been listed on Wealthfront’s Career Launching Companies List and has held a top 10 ranking on the Top 25 Companies Where Women Want to Work.

With a global presence—including offices in San Francisco, Denver, London, Sydney, and Lisbon, plus remote employees worldwide—we are committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and encourage you to apply. Learn more about our story and mission on our Culture and About Us pages. Let’s shape the future of customer engagement together!

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. If this role excites you, please apply — we value applicants for the skills they bring beyond a job description.

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.

Bonus points:

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

Competitive salaries & meaningful equity
Private Medical Insurance
Life/Risk Assurance
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
Paid Sabbatical
Complete laptop workstation

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.

Recruitment Disclaimer:

Please be aware that Iterable, Inc. (“Iterable”) and our official professional recruiting agencies and platforms do not:

Send job offers from free email services like Gmail, Yahoo mail, Hotmail, etc.
Request money, fees, or payment of any kind from prospective candidates to apply to Iterable, for employment, or for the recruitment process (e.g. for home office supplies, or training, etc.).
Request or require personal documents like bank account details, tax forms, or credit card information as part of the recruitment process prior to the candidate signing an engagement letter or an employment contract with Iterable.

You may see all job vacancies on our official Iterable channels:

Official Iterable website, Careers page: https://iterable.com/careers/
Official LinkedIn Jobs page: https://www.linkedin.com/company/iterable/jobs/

Iterable is not affiliated in any way to these impostors and we hereby confirm that such individuals/entities are not authorized, encouraged, or sponsored to act on behalf of Iterable. Such job opportunities are entirely fake and not valid. Therefore, please disregard any written or oral request for a job offer or an interview that you believe is or might be fraudulent or suspicious and immediately reach out to us via email at talent-ops@iterable.com upon receiving a suspicious job offer.

Criminal and/or civil liabilities may arise from such actions, and Iterable expressly reserves the right to take legal action, including criminal action, against such individuals/entities whenever such phenomena occur. In any case, please note that under no circumstances shall Iterable and any of its affiliates be held liable or responsible for any claims, losses, damages, expenses or other inconvenience resulting from or in any way connected to the actions of these impostors.

Iterable is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Iterable does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender-identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Iterable also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Iterable will also consider for employment qualified applicants with arrest and conviction records.