Neural is seeking an experienced and versatile AI/ML & Foundational Model Engineer to design, train, fine-tune, and deploy large-scale language and multimodal models in support of geospatial, aerospace, and mission-critical decision systems. You will work at the forefront of foundation model development, contributing to our internal LLM stack and supporting capabilities like anomaly detection, autonomous reasoning, and dynamic knowledge graphs.

This is a hands-on engineering role requiring deep knowledge of transformers, NLP, computer vision, and annotation/labelling workflows. You’ll collaborate closely with our product, data, and platform teams to build both generalized and domain-adapted AI systems capable of processing text, code, imagery, and spatial data.

• 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

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