Staff Applied ML Engineer

Marigold

Staff Applied ML Engineer at Marigold building and scaling machine learning capabilities across their platforms. Working to improve customer outcomes through predictive models and intelligence features.

Posted 4/24/2026full-timeRemote • 🇦🇺 AustraliaLeadWebsite

Tech Stack

Tools & technologies

AWSCloudPython

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningpredictive modelingfeature engineeringmodel evaluationclassificationregressionrankingrecommendationPythonMLOps

Soft Skills

collaborationproblem-solvingcommunicationadaptabilitycritical thinkingcreativityattention to detailpragmatismcontinuous improvementknowledge sharing