AI / ML Engineer

We bring clarity to physical risk, enabling leaders to engage with confidence and enact resilient business critical decisions. Today's environmental, economic, and infrastructure challenges are deeply interconnected, yet the data required to understand these relationships is scattered across siloed and aging systems. Neural Earth enables operational execution; delivering a single decision intelligence platform that unifies planetary, governmental, and asset-level data, always on and always learning.

This is technical work that requires patience. It requires teams willing to operate at the intersection of AI research, geospatial science, distributed systems, and enterprise deployment. It is also incredibly rewarding. Join us at Neural Earth, the next frontier is here.

Neural Earth is seeking an experienced and versatile AI/ML & Foundational Model Engineer to design, train, fine-tune, and deploy multimodal and geospatially-aware foundation models. You'll contribute to our internal LLM stack and the intelligence layer that powers Neural Earth's conversational AI assistant. Your work will support capabilities such as property risk reasoning, hazard detection, and spatial insight generation across imagery, text, and structured data.

This is a hands-on engineering role requiring deep experience with transformers, multimodal embeddings, and annotation or labelling pipelines. You'll collaborate closely with our product, data, and platform teams to build domain-adapted AI systems that process text, code, imagery, and spatial datasets, and make complex risk intelligence accessible to everyone from underwriters to analysts.

• Architect and train transformer-based models, including BERT, GPT, or vision-language hybrids.
• Build workflows for supervised, unsupervised, and reinforcement learning across NLP and multi-modal tasks.
• Create high-quality datasets with robust labeling/annotation pipelines.
• Fine-tune foundation models for specific use cases (e.g., spatial data parsing, technical document summarization).
• Integrate trained models into production environments via scalable inference services. Monitor performance, perform evaluations, and iterate using continuous feedback loops.
• Publish internal documentation and contribute to research outputs where appropriate.
• Work with raster imagery, geospatial data, time series, video, and audio data. Integrate databases, vector search, data lakes, and streaming data.
• Build agentic AI applications for geospatial and edge computing.

• 3-5+ years of hands-on experience in AI/ML engineering, with a strong portfolio of transformer or LLM-related projects.
• Proficiency with PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent frameworks.
• Experience with labeling tools (e.g., Label Studio, Snorkel) and dataset versioning.
• Strong background in NLP, embeddings, tokenization, attention, and pretraining techniques.
• Understanding of model optimization techniques (e.g., quantization, distillation, LoRA). Ability to work with cross-functional teams on ML deployment.
• Experience with computer vision, segmentation, object recognition, and NLP.

• Experience with geospatial or Earth observation data.
• Familiarity with RAG pipelines, vector databases, and multi-agent LLM orchestration.
• Contributions to open-source LLM projects or relevant academic publications.

The compensation range for this role is $165,000 - $196,000 annually, based on role, level, and expertise.