Job description

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. About This Role As an Automation Engineer at FieldAI , you will work closely with our deployed robot fleet to improve reliability and performance across real-world sites. You’ll sit at the intersection of robotics, machine learning, and field operations , owning the triage and resolution of issues across autonomy.A key part of the role is helping run our ML flywheel : identifying and prioritizing failure modes, curating and labeling data, working with modeling teams to improve models, and partnering with deployment teams to validate and ship those improvements. You’ll also have hands-on ML responsibilities, including building datasets, evaluating models, and training or fine-tuning models for new failure modes or site-specific use cases when needed.This role is ideal for someone who enjoys solving real-world problems, working across teams, and seeing their work directly improve autonomous robots in production. What You’ll Get To Do 1. Site Performance & Failure Triage Own day-to-day triage of issues across deployed sites and identify the highest-impact failure modes. Analyze robot behavior, logs, telemetry, and model outputs to diagnose issues across perception, prediction, and planning. Track failure modes and drive issues through resolution, with a focus on consistently improving site performance. 2. ML Flywheel & Continuous Improvement Work with data/labeling, modeling, and deployment teams to turn field failures into actionable data and model improvements. Build and maintain datasets and evaluation sets for new failure modes and site-specific use cases. Measure improvements in production and ensure fixes are validated and generalized across sites. 3. Hands-on ML & Modeling Train, fine-tune, and evaluate ML models when required to address new failure modes or site-specific challenges. Experiment with data, model, and inference changes across perception, prediction, and learned planning. Help identify whether a problem is best addressed through data, labeling, modeling, or system/deployment changes. 4. Deployment & Site Support Partner with deployment and robotics engineers to integrate and validate model and system improvements on robots. Support new use cases and customer requirements from development through production deployment. Help maintain reliable operation and rapidly burn down issues across deployed sites. What You Have Bachelor’s or Master’s degree in Computer Science, Robotics, AI, or a related field. Strong Python and experience with modern ML frameworks such as PyTorch. Experience training, fine-tuning, and evaluating machine learning models. Strong debugging and problem-solving skills, with the ability to work across ML and robotics systems. Experience with C++ and production ML/robotics systems. Understanding of ML data pipelines, labeling, evaluation, and model deployment. Interest or experience in robotics, autonomous systems, perception, prediction, or planning. Ability to work effectively across modeling, data/labeling, and deployment teams. What Sets you Apart Experience working with real-world robotics, autonomous vehicles, or other deployed ML systems. Experience working with perception, prediction, planning, or learned navigation models. Experience diagnosing model failures and driving iterative improvements from production data.x Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market. Why Join FieldAI in Irvine? In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics’ hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use. You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world. Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like. We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.

Requirements

Must-haves

What You’ll Get To Do
Site Performance & Failure Triage
Own day-to-day triage of issues across deployed sites and identify the highest-impact failure modes.
Analyze robot behavior, logs, telemetry, and model outputs to diagnose issues across perception, prediction, and planning.
Track failure modes and drive issues through resolution, with a focus on consistently improving site performance.
ML Flywheel & Continuous Improvement
Work with data/labeling, modeling, and deployment teams to turn field failures into actionable data and model improvements.
Build and maintain datasets and evaluation sets for new failure modes and site-specific use cases.
Measure improvements in production and ensure fixes are validated and generalized across sites.
Hands-on ML & Modeling
Train, fine-tune, and evaluate ML models when required to address new failure modes or site-specific challenges.
Experiment with data, model, and inference changes across perception, prediction, and learned planning.
Help identify whether a problem is best addressed through data, labeling, modeling, or system/deployment changes.
Deployment & Site Support
Partner with deployment and robotics engineers to integrate and validate model and system improvements on robots.
Support new use cases and customer requirements from development through production deployment.
Help maintain reliable operation and rapidly burn down issues across deployed sites.
What You Have
Bachelor’s or Master’s degree in Computer Science, Robotics, AI, or a related field.
Strong Python and experience with modern ML frameworks such as PyTorch.
Experience training, fine-tuning, and evaluating machine learning models.
Strong debugging and problem-solving skills, with the ability to work across ML and robotics systems.
Experience with C++ and production ML/robotics systems.
Understanding of ML data pipelines, labeling, evaluation, and model deployment.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learningmlpython

Platform: lever
Effort: quick