Senior Machine Learning Engineer, Nova Intelligence

REMOTE - UScontractRemote

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senior

$150k–$250k

H1B: yes

Iterable The Senior Machine Learning Engineer at Iterable designs and builds end-to-end machine learning systems powering the Nova Intelligence platform. The role includes building core ML capabilities such as send-time optimization, recommendation engines, and campaign personalization.

Responsibilities

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.
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.

Qualification

Databricks & Spark masteryProduction-level Model EngineeringProduction CodeSystems & Infra ExperiencePragmatismYou Might Work OnBuilding

Required

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.

Preferred

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.