Staff Applied ML Engineer

Marigold is a fast-growing marketing technology company helping growing businesses build stronger customer relationships through its three core platforms: Emma, Campaign Monitor, and Vuture. We deliver powerful tools for email, SMS, and marketing automation that elevate engagement and drive real results. Marigold is headquartered in Nashville, Tennessee with offices in Sydney and London.

We are seeking a Staff Applied ML Engineer to join our dynamic, fully remote team. You will play a key role in building and scaling machine learning capabilities across our platform, with an initial focus on predictive models and intelligence features that improve customer outcomes.

This is a highly hands-on role. You will design, build, and productionize ML systems that turn our product and historical data into actionable predictions and recommendations. You will partner closely with a Principal Engineer, product teams, and engineers across the organization to integrate these capabilities into core product experiences.

This role complements our broader platform and architectural leadership by bringing deep expertise in applied machine learning, feature engineering, model deployment, and production inference. You will contribute directly to critical systems and help us build a strong foundation for ML-driven product capabilities.

You will also contribute to our broader engineering community by sharing best practices for model development, evaluation, deployment, and iteration.

Work across the Campaign Monitor product to identify valuable opportunities in product and customer data, and turn them into predictive features that improve customer outcomes

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  Turn rich historical product and customer data into predictive features that improve customer outcomes

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  Identify high-impact opportunities for applied machine learning by analyzing product, behavioral, and content data, and translating ambiguous product questions into concrete ML use cases

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  Develop and deploy predictive machine learning models, including models for click-through rate, churn, recommendations, and related engagement signals

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  Design and build features and training datasets from structured product data, historical behavioral data, and content-derived signals

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  Own the applied ML lifecycle from data exploration and feature engineering through training, evaluation, deployment, monitoring, and iteration

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  Build production services and workflows for batch and real-time inference, with a pragmatic focus on reliability, maintainability, and speed to impact

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  Work hands-on in the codebase, contributing to backend systems and product workflows that consume predictions and recommendations

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  Partner closely with product, design, and engineering to turn customer needs into ML-driven product capabilities with measurable business impact

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  Establish pragmatic best practices for model evaluation, experimentation, monitoring, and continuous improvement

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  Help shape how applied machine learning is introduced into the product, while aligning with broader engineering architecture and delivery practices

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  Contribute to shared knowledge across the engineering organization to improve understanding and adoption of applied ML over time

• You are a hands-on engineer who enjoys building and shipping real-world ML systems

• You are comfortable working across data, modeling, and software engineering boundaries

• You make pragmatic decisions, balancing speed, quality, and long-term maintainability

• You take ownership of outcomes, not just models

• You write clean, maintainable code and contribute to a high standard of engineering quality

• You collaborate effectively with engineers, product managers, and designers to solve complex problems

• You are comfortable operating in evolving problem spaces where data quality, ambiguity, and iteration are part of the work

• You are motivated to share knowledge and raise the capability of the broader team

• 7–8+ years building ML systems in production environments 

• Strong experience with applied machine learning for prediction, classification, regression, ranking, or recommendation problems

• Experience with feature engineering, model evaluation, model lifecycle management, and production inference

• Strong experience with Python and common ML tooling

• Experience integrating ML systems into production products at scale

• Strong understanding of backend systems, APIs, data pipelines, and scalable architecture

• Experience with MLOps practices, including deployment, monitoring, retraining, and iteration

• Experience with cloud platforms, preferably AWS

• Experience with growth, marketing, advertising, or recommendation systems

• Experience using LLMs or embeddings to derive content features or enhance product workflows

• Experience designing experiments or working with A/B testing and model performance validation

• Experience building AI or ML enabled SaaS products at scale

Missing a few skills? That’s fine - we value curiosity, growth and the drive to contribute.