Jobs at Neural Earth

Own People Operations for a small global company across two legal entities. Responsibilities include payroll and contractor payments, multi-state tax compliance, HRIS and ATS administration, employee lifecycle management, onboarding and offboarding, leave administration, hiring, candidate screening, compensation, offers, employee support, workforce planning, and culture initiatives.

Open submission for candidates interested in Product Engineering, Data Science, Revenue, or G&A at Neural Earth. Submit your resume and links describing your background and desired roles; submissions are reviewed on a rolling basis. Company focuses on AI research, geospatial science, distributed systems, and enterprise deployment, offering competitive compensation and comprehensive benefits.

Lead end-to-end design for a geospatial AI platform: craft map-based dashboards and AI-assisted workflows, run user research and usability tests, produce production-ready specs and browser prototypes, and partner with engineering and data science to evolve the design system.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Maintain and operate CI/CD pipelines, containerized infrastructure, and AWS cloud environments (ECS/ECR Fargate). Monitor systems, support incident response, perform steady-state operational work, and ramp into existing tooling. Use Terraform/IaC and Python for automation; Kubernetes and cybersecurity experience are valuable. Collaborate with platform team to keep deployments reliable while scaling.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.