[Hiring] Staff Applied ML Engineer @Marigold

Role Description

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.

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

Requirements

• 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

Benefits

• Remote-first, flexible hours, open time away (unlimited annual leave), birthday leave, and strong support for work-life harmony
• Regular team events, Devcamp, hackathons and Culture Club to build genuine relationships and celebrate together
• Clear career progression, mentorship, continuous learning opportunities, and the chance to work at scale on impactful projects
• Generous parental leave, home office setup allowance, salary continuance and life insurance, superannuation, plus access to Sydney office spaces