Staff Applied ML Engineer

This is a fully remote position, open to applicants in Australia.

📋 Description

• Collaborate across the Campaign Monitor product to discover valuable opportunities within product and customer data, transforming them into predictive features that enhance customer outcomes.

• Convert extensive historical product and customer data into predictive features that boost customer results.

• Analyze product, behavioral, and content data to pinpoint high-impact opportunities for applied machine learning, translating vague product inquiries into specific ML use cases.

• Create and implement predictive machine learning models, including those for click-through rates, churn predictions, recommendations, and related engagement indicators.

• Design and develop features and training datasets utilizing structured product data, historical behavioral data, and content-derived signals.

• Manage the entire applied ML lifecycle, from data exploration and feature engineering to training, evaluation, deployment, monitoring, and iteration.

• Build production services and workflows for both batch and real-time inference, emphasizing reliability, maintainability, and rapid impact.

• Actively engage with the codebase, contributing to backend systems and product workflows that utilize predictions and recommendations.

• Collaborate closely with product, design, and engineering teams to transform customer requirements into ML-driven product features that deliver measurable business results.

• Establish practical best practices for model evaluation, experimentation, monitoring, and ongoing enhancement.

• Influence the integration of applied machine learning into the product while aligning with broader engineering architecture and delivery methodologies.

• Share knowledge across the engineering organization to enhance understanding and adoption of applied ML over time.

⛳️ Requirements

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

• Extensive expertise in applied machine learning for prediction, classification, regression, ranking, or recommendation tasks.

• Proficient in feature engineering, model evaluation, model lifecycle management, and production inference.

• Strong command of Python and commonly used ML tools.

• Experience in integrating ML systems into production products at scale.

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

• Familiarity with MLOps practices, including deployment, monitoring, retraining, and iteration.

• Experience with cloud platforms, preferably AWS.

🏝️ Benefits

• Flexibility & Balance: Remote-first approach, 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 foster genuine relationships and celebrate together.

• Professional Growth: Clear career advancement paths, 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, along with access to Sydney office spaces.