Senior ML Backend Engineer

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.

  Requirements

  • 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.

  Benefits

  • 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.