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.
About the Role
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.
Key Responsibilities
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
Turn rich historical product and customer data into predictive features that improve customer outcomes
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
Develop and deploy predictive machine learning models, including models for click-through rate, churn, recommendations, and related engagement signals
Design and build features and training datasets from structured product data, historical behavioral data, and content-derived signals
Own the applied ML lifecycle from data exploration and feature engineering through training, evaluation, deployment, monitoring, and iteration
Build production services and workflows for batch and real-time inference, with a pragmatic focus on reliability, maintainability, and speed to impact
Work hands-on in the codebase, contributing to backend systems and product workflows that consume predictions and recommendations
Partner closely with product, design, and engineering to turn customer needs into ML-driven product capabilities with measurable business impact
Establish pragmatic best practices for model evaluation, experimentation, monitoring, and continuous improvement
Help shape how applied machine learning is introduced into the product, while aligning with broader engineering architecture and delivery practices
Contribute to shared knowledge across the engineering organization to improve understanding and adoption of applied ML over time
About You
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
Ideal Qualifications
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
Nice to Have
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.
What We Offer
Flexibility & Balance: Remote-first, flexible hours, open time away (unlimited annual leave), birthday leave, and strong support for work-life harmony.
Connection & Culture: Regular team events, Devcamp, hackathons and Culture Club to build genuine relationships and celebrate together.
Professional Growth: Clear career progression, mentorship, continuous learning opportunities, and the chance to work at scale on impactful projects.
Support & Benefits: Generous parental leave, home office setup allowance, salary continuance and life insurance, superannuation, plus access to Sydney office spaces.