AI/ML Engineer

Neural; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

A leading company in the AI sector is seeking an experienced AI/ML & Foundational Model Engineer to join their innovative team. You will have the opportunity to work at the forefront of AI, designing and deploying cutting-edge models for geospatial and mission-critical systems. Your role will be dynamic, engaging with advanced technologies and methodologies, helping to shape the future of AI. If you possess a strong background in machine learning and AI development, your skills will be instrumental in driving our projects forward.

Qualifications

• 3-5+ years of experience in AI/ML engineering with a strong portfolio.
• Proficient in PyTorch, TensorFlow, and equivalent frameworks.
• Background in NLP, embeddings, and model optimization techniques.

Responsibilities

• Design and deploy models for NLP and multimodal tasks.
• Integrate models into production via inference services.
• Build and manage high-quality datasets.

Skills

NLP

Computer Vision

Transformers

Machine Learning

Deep Learning

Data Annotation

Tools

PyTorch

TensorFlow

Hugging Face

LangChain

Label Studio

Snorkel

Job description

Contract / Contract to full time / Full time

Remote

About the team

At Neural, we are committed to building the future of AI. The world is changing, and we're at the forefront of that transformation. Our team is dedicated to creating innovative solutions that address the unique challenges of today's dynamic industries and unlock the potential of new markets.

We harness the power of Artificial Intelligence and Machine Learning to drive innovation and create solutions that shape the future of industries.We believe that the future of AI is in your hands. Our mission is to empower individuals and organizations to harness the power of AI to achieve their goals. Join us in shaping the future of AI today.

About the position

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.

Responsibilities

• 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

Qualification

• 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
  

Preferred

• 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.