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Transforming industrial operations through physically embodied robotic AI and superhuman autonomy.
The Role
Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.
Summary Generated by Built In
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
Modern ML systems improve through their data flywheel: production experience generates new data, that data is processed and understood, valuable examples are identified and labeled, datasets are created, models are retrained and evaluated, and the resulting models return to production.
We are looking for an ML Engineer focused on the Data Flywheel to help build and automate this lifecycle for autonomous robots operating in complex real-world environments.
This role sits between ML engineering and data engineering. You will work closely with perception/autonomy researchers, labeling, Data Platform, and ML Platform engineers to turn large volumes of robot experience into high-quality training data.
What You'll Get to Do
- Build and maintain pipelines that transform robot data into training-ready ML datasets.
- Automate data processing, filtering, selection, transformation, labeling, and dataset-generation workflows.
- Develop scalable batch and offline inference pipelines for auto-labeling and data mining.
- Build systems for selecting useful or difficult examples from large volumes of robot data.
- Integrate manual labeling and model-assisted/automated labeling into repeatable data workflows.
- Implement data-quality checks and dataset validation.
- Build reproducible and versioned dataset-generation pipelines.
- Improve incremental dataset generation so new robot experience can efficiently feed future training cycles.
- Partner with researchers to translate experimental data-processing logic into reliable production pipelines.
- Measure and improve the efficiency of the loop from new robot experience to usable training data.
What We're Looking For
- Strong Python and software engineering skills.
- Experience building ML/data-processing pipelines.
- Understanding of ML dataset construction and training workflows.
- Experience processing large datasets using distributed or parallel computing.
- Experience with object storage, Parquet or similar formats, workflow systems, and cloud compute.
- Ability to bridge exploratory ML code and reliable production systems.
- Strong understanding of data quality and reproducibility.
Nice to Have
- Computer vision, perception, robotics, or multimodal ML experience.
- Experience with auto-labeling, active learning, hard-example mining, or data selection.
- Experience processing images, video, point clouds, LiDAR, or other sensor data.
- Ray/Spark or similar distributed-processing experience.
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.
Skills Required
- Strong Python and software engineering skills
- Experience building ML or data-processing pipelines
- Understanding of ML dataset construction and training workflows
- Experience processing large datasets using distributed or parallel computing
- Experience with object storage, Parquet or similar formats, workflow systems, and cloud compute
- Ability to bridge exploratory ML code and reliable production systems
- Strong understanding of data quality and reproducibility
- Computer vision, perception, robotics, or multimodal ML experience
- Experience with auto-labeling, active learning, hard-example mining, or data selection
- Experience processing images, video, point clouds, LiDAR, or other sensor data
- Ray, Spark, or similar distributed-processing experience
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The Company
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.
