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.

As an ML Engineer at FieldAI, you will help build the next-generation Field Foundation Model (FFM), powering a global fleet of autonomous robots deployed across diverse environments. Your contributions will directly shape how we scale – through advances in model architecture, training methodologies, and deployment strategies.

You’ll collaborate closely with machine learning scientists, software engineers, and robotics experts to design and implement FFM capabilities that generalize across tasks and environments. Beyond model development, you’ll also support deployment and monitoring to ensure smooth integration and reliable real-world performance.

This role offers the opportunity to work with cutting-edge technologies, solve complex challenges, and directly impact large-scale robot deployments.

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.

Publications in top tier ML or robotics conferences