# Machine Learning Engineer at Iterable > Hybrid · Portugal · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aiterable%3Adebb4743-b0ce-482d-9695-577c8df82db4 (read 2026-11-04). ## Facts (observed) - Company: [Iterable](https://alderhq.app/companies/iterable) - Location text: Lisbon - Where it can be done from (inferred): Hybrid · Portugal (evidence: "Location: Lisbon") - Posted: 2026-11-03 (1 days ago) - First seen by alder: 2026-11-03 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/iterable/debb4743-b0ce-482d-9695-577c8df82db4 - Skills listed (inferred): Python, Scala, AWS, Kubernetes, Docker, Spark, Snowflake, Distributed systems ## Pay and sponsorship - Posted pay: €55k–€69k per year (from the listing's pay field) - Market band for similar posted roles (Lisbon, n 3): p25 €65k, median €68k, p75 €73k - H-1B filings at this company for similar roles, FY26: 1 ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) - Licensed UK sponsor: registered as "Iterable Limited" (checked 2026-11-03, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers)) ## Description (the employer's text) Iterable is the AI customer engagement platform, built for enterprise scale, loved by teams, and trusted by global brands like Redfin, SeatGeek, Priceline, Calm, and Box. Our platform empowers organizations to activate customer data from any source, design seamless cross-channel experiences, and optimize engagement with Nova Intelligence, the AI layer of our platform, 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 to life every day: Be an Owner, Growth Mindset, Run as One, and Transparency. And the market has noticed: G2 recognizes Iterable as a Leader globally, spanning the Americas, EMEA, APAC, ANZ, and Latin America, with category leadership in Personalization, Marketing Automation, Push Notifications, and Mobile Marketing, and a 4.5/5-star rating from hundreds of users. Gartner named Iterable a Challenger in the Gartner Magic Quadrant for Multichannel Marketing in both 2024 and 2025. We’ve also earned TrustRadius Top Rated honors in 2024 and 2025, were named a TrustRadius Buyer’s Choice for 2026, and were featured in Snowflake’s Modern Marketing Data Stack 2026. With a global presence that includes 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 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 ML Capabilities: Contribute to core product features, including send-time/frequency optimization, recommendation engines, and campaign personalization. Execute Full Model Lifecycle: Perform EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., A/B testing, multi-armed bandits, ensemble models). Maintain & Scale Pipelines: Build and maintain scalable data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes. Collaborate Cross-Functionally: Partner with Backend and Platform teams to integrate feature serving, improve data architecture, and adhere to team engineering standards. Continuous Learning: Participate in design reviews, expand your domain knowledge, and continuously improve code quality across our repositories. ### What We're Looking For Experience: 2–4 years of hands-on MLE experience deploying models into production environments. Data Processing & Scaling: Practical experience building and optimizing production data pipelines using Databricks and Spark. Production Model Engineering: Solid background in training, evaluating, optimizing, and serving machine learning and deep learning models in production. Production Code: Proficiency in Python and/or Scala with a strong focus on clean, modular, tested, and maintainable code. Systems & Infrastructure: Hands-on experience with containerization (Docker, Kubernetes), distributed compute frameworks (Ray or Spark), and microservices. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aiterable%3Adebb4743-b0ce-482d-9695-577c8df82db4 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.