Senior Machine Learning Engineer

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence.

If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

Machine Learning modeling

• Design, train, and deploy state-of-the-art machine learning models for end-to-end learning based navigation stack.
• Work with deep learning architectures such as transformers, convolutional networks to capture complex decision making.
• Architect and implement full-stack end-to-end navigation solutions, covering perception, prediction, and planning.
• Explore novel data generation and collection pipelines to enrich training datasets. Model Deployment, Monitoring & Performance
• Assist with deploying machine learning models into production environments
• Continuously monitor models in production, detecting model drift, and automating retraining processes as applicable
• Troubleshoot issues related to model deployment, performance, and system integration.

Bachelor's or Master's degree in Computer Science, AI, Statistics, or a related field, with 4+ years of industry experience.

• Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX, alongside working knowledge of C++ for deployment and system integration.
• Deep understanding of contemporary deep learning architectures, optimization, and evaluation, with a strong grasp of end-to-end navigation stack components — including perception, prediction, and path/motion planning.
• Proven track record deploying ML models into production environments, ideally within robotics, self-driving, or NLP.

• Publications in top tier ML or robotics conferences