Senior ML Engineer — Data Flywheel
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. The posting includes: About This Role (description), What You'll Get to Do (responsibilities), What We're Looking For (requirements/qualifications/skills), Nice to Have (maybe skills), Why Join FieldAI in Irvine? (benefits? maybe description of workplace). Salary range: "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." This is vague, not specific numbers, but it's still some content about pay. The instruction: never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state salary range is generous, base pay may vary. That's some content, albeit vague. Should we include Pay section? It says "Our salary range is generous and we consider each individual’s background and experience when determining final compensation." That's not a specific number but it's still stating something about pay. The instruction: "never emit a heading followed by filler/placeholder text (e.g. 'commensurate with experience', 'not specified', 'N/A', 'varies', 'TBD')". The phrase "Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market." is similar to "varies". That might be considered filler. The instruction says never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state something about pay, but it's vague. However, the instruction also says never emit a heading followed by filler/placeholder text like "varies". So if we include Pay section with that vague statement, it would be filler. Better to omit Pay section. Benefits: The posting includes "Why Join FieldAI in Irvine?" which describes benefits: work where robots are, real hardware, shipping, etc. That's more like benefits/culture. Could be a Benefits section. Also there is mention of "We value diverse perspectives..." that's EEO boilerplate, should be removed. Also mention of AI tools in hiring process, also boilerplate? Might be considered legal/EEO? It's about hiring process, maybe keep? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The AI tools statement is not exactly EEO but could be considered hiring process info; not sure if to keep. Probably safe to keep as part of "Why Join" or separate? But we should not create sections unless there is real specific content. The "Why Join FieldAI in Irvine?" includes specific content about work environment, team background, etc. That could be a Benefits section or maybe a "About the team" but we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can map: About the role -> "About This Role". Responsibilities -> "What You'll Get to Do". Requirements/Qualifications/Skills -> combine "What We're Looking For" and "Nice to Have". Benefits -> "Why Join FieldAI in Irvine?" (culture, benefits). Pay and Schedule omitted because no specific info.
We need to output HTML fragment only, no markdown fences.
We'll produce:
About the role
... text from About This Role ...
Responsibilities
- ... each bullet from What You'll Get to Do ...
Requirements
- ... each bullet from What We're Looking For ...
Skills
- ... each bullet from Nice to Have ...
Benefits
... text from Why Join FieldAI in Irvine? (excluding EEO boilerplate) ...
We need to preserve all substantive information, not invent or summarize away detail. So we need to copy the text exactly? We can paraphrase? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. So we should keep the original sentences, maybe split into paragraphs or list items. We should not omit any detail. However, we can format asfor prose and
- for lists. For the "About This Role" paragraph, we can keep as a
. For responsibilities, each sentence starting with a verb can be a list item. We need to ensure we don't lose any detail. Let's extract the relevant sections. Original text: 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. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. We need to remove boilerplate: EEO statement, AI tools statement, maybe the final sentence about contact us. Also remove duplicated lines? Not seeing duplicates. We need to keep all substantive info: The description about Irvine team, About This Role, What You'll Get to Do, What We're Looking For, Nice to Have, Why Join FieldAI in Irvine? (including the subparts: In Irvine..., We are solving..., Our Field Foundational Models..., You will collaborate..., We bring deep experience..., Be Part of the Next Robotics Revolution..., Our teams span..., Our headquarters is in Irvine..., Join us in Southern California..., We value diverse perspectives... (that's EEO, remove), We may use AI tools... (remove), If you would like more information... (remove). Also remove "Our salary range is generous..." maybe we keep? It's about pay but vague. According to instruction, we should not create Pay section unless posting actually states them. It does state something about pay but it's vague. The instruction says never emit a heading followed by filler/placeholder text like "varies". So if we include Pay section with that text, it's filler. Safer to omit Pay section entirely. Also we should not include Schedule section (none). Thus we will have sections: About the role, Responsibilities, Requirements, Skills, Benefits. Now we need to copy the substantive content exactly, but we can break into
or
- . Must not invent or summarize away detail. So we need to include all sentences from those sections. Let's extract each section's content. About This Role: starts after "About This Role". The text: "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." We need to keep that as a paragraph. What You'll Get to Do: list of bullet points (each line starts with a verb). The text: "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