Field Ai

<h2>What You'll Get To Do</h2><div>

<li>Design and build the data platform, frameworks, and developer tooling that power ingestion across Field AI.</li>
<li>Handle the realities of field data: intermittent connectivity, large sensor payloads (LiDAR, camera, IMU), edge-to-cloud synchronization, and backfill from offline deployments.</li>
<li>Develop reusable ingestion SDKs, APIs, and services that enable teams to onboard new robotics data sources with minimal custom code.</li>
<li>Build and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, simulation outputs, and cloud object storage.</li>
<li>Integrate the platform with downstream consumers: BI tools, ML training and evaluation pipelines, labeling systems, and issue tracking.</li>
<li>Develop connectors and APIs (REST/gRPC, webhooks, CDC) so internal teams can feed data in and consume curated datasets reliably.</li>
<li>Own integration reliability end to end: schema contracts, versioning, retries, backfills, and monitoring.</li>
<li>Optimize pipeline performance, scalability, and cost across growing fleet deployments.</li>

</div>
<h2>What You Have</h2><div>

<li>Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.</li>
<li>3–5+ years of experience in <strong>data engineering or backend engineering</strong> focused on pipelines and infrastructure.</li>
<li>Strong programming skills in <strong>Python and SQL</strong> (C++, Scala, or Java a plus).</li>
<li>Production experience with <strong>streaming systems (Kafka, Kinesis, Pub/Sub)</strong> and orchestration tools such as <strong>Airflow or Dagster</strong>.</li>
<li>Experience with a modern <strong>warehouse or lakehouse (BigQuery, Snowflake, Databricks, Redshift)</strong> and cloud object storage at scale.</li>
<li>Experience building integrations across systems: third-party APIs, internal services, and <strong>CDC/ELT tooling (Fivetran, Airbyte, Debezium, or custom connectors)</strong>.</li>
<li>Experience building for data quality: testing, monitoring, lineage, and incident response.</li>
<li>Strong problem-solving skills and ability to work in interdisciplinary teams.</li>

</div>
<h2>The Extras That Set You Apart</h2><div>

<li>Experience with <strong>robotics, autonomy, automotive, or other telemetry-heavy operational data</strong> (bag files, fleet logs, time-series sensor data).</li>
<li>Familiarity with robotics middleware and log formats such as <strong>ROS/ROS2, MCAP, or rosbag</strong>.</li>

</div>

Data Platform Engineer, Autonomy Analytics

<div>
<p><strong>Location: Irvine, CA</strong></p>
<p><a href="https://www.fieldai.com/" rel="noopener noreferrer ugc" target="_blank">Field AI</a> is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven(1) approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.</p>
<p><strong>About Field AI</strong></p>
<p>Field AI is at the forefront of robotic embodied AI, transforming industries like construction, security, mining, and manufacturing. Our autonomous robots operate globally, often in harsh environments, delivering critical insights to customers. Whether monitoring construction progress, ensuring safety compliance, or conducting predictive maintenance, Field AI is advancing technology to make a meaningful impact.</p>
<p>Learn more at <a href="https://fieldai.com/" rel="noopener noreferrer ugc" target="_blank">https://fieldai.com</a>.</p>
<p><strong>About the Data Platform Team</strong></p>
<p> Every robot we deploy generates a continuous stream of telemetry, sensor logs, and operational data from environments around the world. The Data Platform team builds the systems that capture every autonomy intervention, anomaly, and operational signal from globally deployed robots, classify them, and turn them into the ranked problem list that drives our engineering roadmap.</p>
<p><strong>About the Job</strong></p>
<p>As a <strong>Data Platform Engineer, Data Pipelines</strong>, you will design and build the systems and integrations that help move data reliably from robots in the field to the teams and services that depend on it — analytics, autonomy, ML training, and deployment operations. You will collaborate with cross-functional teams spanning robotics, autonomy, and deployment to build the data backbone of a field robotics company.</p>
</div>
<h2>What You'll Get To Do</h2><div>

<li>Design and build the data platform, frameworks, and developer tooling that power ingestion across Field AI.</li>
<li>Handle the realities of field data: intermittent connectivity, large sensor payloads (LiDAR, camera, IMU), edge-to-cloud synchronization, and backfill from offline deployments.</li>
<li>Develop reusable ingestion SDKs, APIs, and services that enable teams to onboard new robotics data sources with minimal custom code.</li>
<li>Build and maintain integrations across heterogeneous sources: robot/edge systems, fleet management and deployment tooling, simulation outputs, and cloud object storage.</li>
<li>Integrate the platform with downstream consumers: BI tools, ML training and evaluation pipelines, labeling systems, and issue tracking.</li>
<li>Develop connectors and APIs (REST/gRPC, webhooks, CDC) so internal teams can feed data in and consume curated datasets reliably.</li>
<li>Own integration reliability end to end: schema contracts, versioning, retries, backfills, and monitoring.</li>
<li>Optimize pipeline performance, scalability, and cost across growing fleet deployments.</li>

</div>
<h2>What You Have</h2><div>

<li>Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.</li>
<li>3–5+ years of experience in <strong>data engineering or backend engineering</strong> focused on pipelines and infrastructure.</li>
<li>Strong programming skills in <strong>Python and SQL</strong> (C++, Scala, or Java a plus).</li>
<li>Production experience with <strong>streaming systems (Kafka, Kinesis, Pub/Sub)</strong> and orchestration tools such as <strong>Airflow or Dagster</strong>.</li>
<li>Experience with a modern <strong>warehouse or lakehouse (BigQuery, Snowflake, Databricks, Redshift)</strong> and cloud object storage at scale.</li>
<li>Experience building integrations across systems: third-party APIs, internal services, and <strong>CDC/ELT tooling (Fivetran, Airbyte, Debezium, or custom connectors)</strong>.</li>
<li>Experience building for data quality: testing, monitoring, lineage, and incident response.</li>
<li>Strong problem-solving skills and ability to work in interdisciplinary teams.</li>

</div>
<h2>The Extras That Set You Apart</h2><div>

<li>Experience with <strong>robotics, autonomy, automotive, or other telemetry-heavy operational data</strong> (bag files, fleet logs, time-series sensor data).</li>
<li>Familiarity with robotics middleware and log formats such as <strong>ROS/ROS2, MCAP, or rosbag</strong>.</li>
<li>Experience with edge computing or intermittently connected data collection.</li>
<li>Experience with <strong>dbt</strong> or similar transformation frameworks.</li>

</div>

Data Platform Engineer, Data Pipelines

<div><a href="http://www.fieldai.com" rel="noopener noreferrer ugc" target="_blank">FieldAI</a>’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.</div>
<h2>What You’ll Get To Do</h2><div>
<p><strong>Machine Learning modeling</strong></p>

<li>Design, train, and deploy state-of-the-art machine learning models for end-to-end learning based navigation stack.</li>
<li>Work with deep learning architectures such as transformers, convolutional networks to capture complex decision making. </li>
<li>Architect and implement full-stack end-to-end navigation solutions, covering perception, prediction, and planning.</li>
<li>Explore novel data generation and collection pipelines to enrich training datasets.</li>

<p><strong>Model Deployment, Monitoring &amp; Performance</strong></p>

<li>Assist with deploying machine learning models into production environments</li>
<li>Continuously monitor models in production, detecting model drift, and automating retraining processes as applicable</li>
<li>Troubleshoot issues related to model deployment, performance, and system integration.</li>

</div>
<h2>What You Have</h2><p>Bachelor's or Master's degree in Computer Science, AI, Statistics, or a related field, with 4+ years of industry experience.</p>

<li>Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX, alongside working knowledge of C++ for deployment and system integration.</li>
<li>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.</li>
<li>Proven track record deploying ML models into production environments, ideally within robotics, self-driving, or NLP.</li>
<h2>The Extras That Set You Apart</h2><div>

<li><span>Publications in top tier ML or robotics conferences</span></li>

</div>

Senior Machine Learning Engineer

<div><a href="http://www.fieldai.com" rel="noopener noreferrer ugc" target="_blank">FieldAI</a>’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.</div>
<h2>What You’ll Do</h2><ul><li>Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.</li>
<li>Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).</li>
<li>Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.</li>
<li>Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).</li>
<li>Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.</li></ul>
<h2>What You Have</h2><ul><li>Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.</li>
<li>2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.</li>
<li>Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.</li>
<li>Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.</li>
<li>Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.</li>
<li>Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.</li>
<li>Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.</li>
<li>Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.</li></ul>
<h2>The Extras That Set You Apart</h2><ul><li>Experience working with BIM data, digital twins, or construction-related sensor data.</li>
<li>Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.</li>
<li>Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.</li>
<li>Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.</li>
<li>Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).</li>
<li>Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.</li>
<li>Experience building custom modules for SparseConvNet or 3D transformers.</li></ul>

Field Ai

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Data Platform Engineer, Autonomy Analytics

Data Platform Engineer, Data Pipelines

Senior Machine Learning Engineer

3D Machine Learning Engineer

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