Remote Senior Applied Machine Learning Engineer - Applied Machine Learning Team in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Remote Senior Applied Machine Learning Engineer - Applied Machine Learning Team in United States.

This role sits at the core of a high-impact Applied Machine Learning organization focused on transforming large-scale real estate data into intelligent, customer-facing products. You will help bring research-grade machine learning models into production systems that serve millions of users, powering key experiences such as home valuation and property recommendations. Working in a highly data-driven and engineering-heavy environment, you will bridge the gap between research and production, ensuring models are scalable, reliable, and continuously improving. The role involves working with large, multimodal datasets including text, images, video, and 3D scans. You will also contribute to building robust MLOps infrastructure that supports automated deployment, monitoring, and retraining at scale. This is a highly collaborative position where your work directly influences customer experience and business outcomes in a fast-paced, product-focused engineering culture.

In this role, you will take ownership of bringing machine learning models into scalable, production-ready systems while ensuring long-term performance and reliability:

• Transform research and prototype models into efficient, maintainable production-grade systems
• Design and implement MLOps pipelines including CI/CD for ML, automated retraining, and model versioning
• Optimize models for real-time inference, ensuring speed, scalability, and system efficiency
• Build and maintain monitoring systems to detect data drift, model degradation, and latency issues
• Collaborate on the development of automated valuation models and recommendation systems
• Continuously improve ML models powering customer-facing features at scale
• Serve as a technical bridge between data science, engineering, and product stakeholders
• Develop data products and tools that drive key business metrics and revenue impact

The ideal candidate brings strong software engineering experience combined with hands-on expertise in deploying and scaling machine learning systems in production environments:

• 5+ years of software engineering experience, including at least 2 years in production ML systems
• Strong proficiency in Python and ability to build production-quality, modular codebases
• Deep understanding of the ML lifecycle, including training, inference, feature stores, and model versioning
• Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn for tuning and optimization
• Hands-on experience with Docker and Kubernetes for deploying and scaling containerized services
• Strong knowledge of MLOps practices, including CI/CD, monitoring, logging, and alerting systems
• Experience with model monitoring, A/B testing, and performance tracking in production
• Proficiency in SQL and familiarity with distributed data tools such as Spark or Kafka
• Strong debugging skills and ability to ensure reliability in high-scale systems
• Excellent collaboration and communication skills for cross-functional technical alignment

• Competitive compensation package ($164,300 – $258,100 base salary range)
• Eligibility for annual bonus, incentives, and performance-based rewards
• Comprehensive healthcare coverage including medical, dental, and vision
• 401(k) retirement plan with employer participation
• Paid time off and flexible leave policies
• Family-focused benefits supporting well-being and work-life balance
• Remote-friendly work structure with optional hybrid presence in Seattle
• Access to engineering-scale ML infrastructure and high-impact data systems