Senior ML Backend Engineer

This role offers the opportunity to build and evolve the machine learning infrastructure behind advanced property intelligence solutions. You will combine strong backend engineering expertise with machine learning operations to create scalable platforms for training, evaluation, deployment, and monitoring. Working alongside machine learning engineers, researchers, and software teams, you will help move innovative models from experimentation into reliable production systems. The position involves cloud-native technologies, distributed computing, automation, observability, and responsible AI practices. You will also explore AI-powered engineering tools and modern technologies that improve development efficiency and operational performance. Your work will contribute to solutions that generate insights from large-scale aerial and satellite imagery while helping organizations better understand climate and economic risks. Accountabilities

• Build, maintain, and evolve scalable machine learning infrastructure supporting model development, training, evaluation, deployment, and monitoring.
• Design reliable, cost-effective ML pipelines, platforms, and engineering tooling for large-scale workloads.
• Partner with machine learning engineers and researchers to transition new models and approaches from prototypes into production-ready systems.
• Develop automation, testing, observability, monitoring, reproducibility, data lineage, and governance capabilities across ML environments.
• Evaluate and integrate new data sources, technologies, platforms, and tools that can improve model performance and operational efficiency.
• Collaborate with software engineering, technology, product, and commercial stakeholders to deliver scalable machine learning solutions.
• Apply AI-powered development tools, coding assistants, and LLM-based agents to automate workflows and improve engineering productivity.
• Ensure machine learning systems meet security, governance, responsible AI, and model risk management standards.
• Identify technical trade-offs and contribute to architectural decisions involving infrastructure scalability, reliability, performance, and cost., * Opportunity to work on advanced machine learning, computer vision, geospatial analytics, and AI challenges.
• Exposure to large-scale aerial and satellite imagery and technology supporting property intelligence solutions.
• Work with modern cloud-native infrastructure, distributed computing, ML platforms, and AI-enabled engineering tools.
• Opportunity to contribute to responsible AI, model governance, security, and risk management practices.
• Collaborative environment involving machine learning engineers, researchers, software engineers, product teams, and other stakeholders.
• Professional growth opportunities through work on complex, high-impact machine learning infrastructure challenges.
• Inclusive workplace focused on curiosity, diverse perspectives, integrity, collaboration, and continuous improvement.
• Candidates who do not meet every listed requirement are encouraged to apply if they can demonstrate relevant skills and experience.

• Senior-level backend software engineering experience with a strong understanding of machine learning and a demonstrated interest in developing deeper ML expertise.
• Experience designing, building, and maintaining machine learning infrastructure, platforms, and tooling for large-scale training, evaluation, and deployment.
• Strong proficiency in Python and modern machine learning engineering tools, including deep learning frameworks, experiment tracking, version control, containerization, and automated workflows.
• Hands-on experience with MLOps practices such as CI/CD, model monitoring, reproducibility, data lineage, model governance, and production operations.
• Proven experience with cloud-native technologies, Kubernetes, distributed computing environments, and scalable infrastructure supporting machine learning workloads.
• Demonstrated understanding of artificial intelligence concepts and practical experience using AI tools, coding assistants, and LLM-based agents to enhance engineering workflows.
• Experience implementing AI-powered solutions to address business challenges, with awareness of responsible and ethical AI principles.
• Strong analytical, problem-solving, and communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Ability to collaborate effectively with machine learning engineers, researchers, software developers, product teams, and business stakeholders.
• PhD in a science, technology, engineering, or mathematics discipline is preferred; a Master’s degree with significant relevant industry experience or a Bachelor’s degree with extensive hands-on experience is also considered.
• Equivalent practical experience and non-traditional career paths are welcome.