Robot Learning Engineer - Manipulation

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Need to extract content from posting. First, remove boilerplate: company description, location list, etc. Keep role description. We need to identify sections: - About The Role: there is a heading "About The Role". Content after that: description of robot learning platform, tasks. We'll put that in

About the role

then

... maybe multiple paragraphs. - Responsibilities: The bullet list starting with "At Applied Intuition, You Will Work on the full learning loop..." and subsequent bullet-like sentences. Those are responsibilities. We'll put under

Responsibilities

with

• each. - Requirements / Qualifications / Skills: The "We're Looking For Someone Who Has ..." list and "Nice To Have" list. We need to decide sections. Could combine Requirements (must-have) and Nice To Have as separate sections? The instruction: prefer few substantive sections over many thin ones. Could have Requirements (must) and Skills (nice-to-have) or Qualifications. Let's see: Must-have list: trained/fine-tuned manipulation policy, strong Python/PyTorch, practical depth in imitation learning and at least one modern policy family, working knowledge of robot kinematics..., habit of diagnosing failures..., comfort taking on open-ended problems... That's requirements. Nice-to-have list: experience with bimanual manipulation, force-controlled insertion, tactile sensing, dexterous hands; ROS2, LeRobot, simulators; optimizing inference on NVIDIA Jetson; RL post-training, learning from interventions, transferring policies. We could put Requirements as must-have, Skills as nice-to-have. Or Qualifications as must-have and Skills as nice-to-have. We'll decide:

  Requirements

  for must-have,

  Skills

  for nice-to-have. - Benefits: The paragraph about compensation includes base salary, equity, benefits list: comprehensive health, dental, vision, life and disability insurance, 401k with match, learning and wellness stipends, paid time off. Also note benefits subject to change. That's benefits content. So we can have

  Benefits

  with
  • each. - Pay: The compensation range: $150K - $300K. Also mention base salary component etc. We have specific pay range. So we can include Pay section with that range. Also mention base salary is component of total compensation. We'll include that. - Schedule: The posting mentions "We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions". That's schedule info. So we can have Schedule section. We must not repeat job title as heading. Not include Job ID etc. We must not include boilerplate like EEO, legal, etc. Remove those paragraphs. We must preserve all substantive information. Ensure we don't omit details. Let's extract exact text for each section. About the role: Starting after "About The Role". The text: "Applied Intuition is building a robot learning platform on Dana, its physical AI platform: the data infrastructure and training intelligence a company needs to make any robot learn an industrial task and keep improving it. The robotics team builds that platform and uses it to deliver robot autonomy on customer lines, training, evaluating, and deploying policies on real robots doing real industrial tasks. Everyone on the team works hands-on with hardware and sees their work reach customers. As a Robot Learning Engineer, you will work on manipulation policies from task definition to a policy running reliably on a physical robot. You will train and fine-tune policies, understand why they fail, and make them dependable enough for customer use. Success is measured on the robot, not only on offline benchmarks. We are open to candidates at different experience levels who meet the requirements. We value demonstrated work on real systems, including equivalent practical experience, over a particular degree or title." Then after that there is "At Applied Intuition, You Will Work on the full learning loop for manipulation tasks: task definition, demonstration collection, data curation, training, real-robot evaluation, and deployment." That seems like responsibilities. We'll separate. Thus About the role paragraph ends before "At Applied Intuition, You Will Work..." We'll keep that as About. Now Responsibilities: The list after that: - Train and fine-tune manipulation policies, from large pretrained models such as vision-language-action models to compact task-specific policies, and choose the right approach for each task. - Develop repeatable recipes for industrial tasks such as pick-and-place, bimanual handling, and contact-rich assembly, adding force or tactile signals where they help. - Deploy policies on edge compute and validate observation processing, action interfaces, and control timing on the robot. - Turn failures and human interventions into better data, better models, and better evaluation. - Measure what customers care about, including success rate, cycle time, intervention rate, and the data and time needed to reach a target, and improve those numbers task after task. - Package recipes and models so the next task, and the next robot, starts from what was learned on the last one. We'll put each as
  • . Now Requirements (must-have): The list after "We're Looking For Someone Who Has": - Trained or fine-tuned a learned manipulation policy and deployed and evaluated it on a physical robot. - Strong Python and PyTorch skills, with the ability to write maintainable training, evaluation, and deployment code. - Practical depth in imitation learning and at least one modern policy family, such as vision-language-action models, diffusion policies, or action-chunking transformers. - Working knowledge of robot kinematics, coordinate frames, camera calibration, and the interface between learned actions and low-level control. - The habit of diagnosing failures with controlled experiments across data, sensing, model, and execution. - Comfort taking on open-ended problems and communicating tradeoffs clearly to teammates at the robot. Now Skills (nice-to-have): list after "Nice To Have": - Experience with bimanual manipulation, force-controlled insertion, tactile sensing, or dexterous hands. - Experience with ROS 2, LeRobot, or simulators such as Isaac and MuJoCo. - Experience optimizing inference on NVIDIA Jetson or GPU edge systems, including model export, compilation, or quantization. - Experience with reinforcement learning post-training, learning from interventions, or transferring policies across robot platforms. Now Benefits: The paragraph after "Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment." We'll extract benefits items: equity (options/RSU), comprehensive health, dental, vision, life and disability insurance, 401k retirement benefits with employer match, learning and wellness stipends, paid time off. Also base salary is component but that's pay. We'll list benefits as
    • each. Now Pay: The compensation range: "Compensation Range: $150K - $300K". Also earlier: "Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position." We'll include Pay section with that range and note that base salary is component of total compensation. Now Schedule: The paragraph about in-office expectation: "We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions" We'll put that as Schedule. Now ensure we don't include any boilerplate like EEO, legal, etc. Remove those paragraphs. Now produce HTML fragment: only sections with headings

      and content. We'll order: About the role, Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. Make sure to use

      for prose,

      • for lists. Let's craft. About the role: combine the two paragraphs? Actually we separated responsibilities. So About the role paragraph is first paragraph before responsibilities. We'll output that as

        . Now Responsibilities:

        • each. Now Requirements:
          • each. Now Skills:
            • each. Now Benefits:
              • each. Now Pay:

                with range and note. Now Schedule:

                with description. Check for any repeated content: ensure we didn't include the "At Applied Intuition, You Will Work on the full learning loop..." as responsibilities; we included that as first bullet. Now verify we didn't miss any substantive info: The line "We are open to candidates at different experience levels who meet the requirements. We value demonstrated work on real systems, including equivalent practical experience, over a particular degree or title." That's in About the role; we included. The line "Success is measured on the robot, not only on offline benchmarks." included. The line "As a Robot Learning Engineer, you will work on manipulation policies from task definition to a policy running reliably on a physical robot. You will train and fine-tune policies, understand why they fail, and make them dependable enough for customer use." included. Now check for any other substantive: The line about "Eighteen of the top 20 global automakers..." etc is company description, we removed as boilerplate? It's not required but maybe considered background; but instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Company description may be considered not required but could be considered substantive? The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." The company description is substantive but maybe considered background; but they didn't explicitly say to remove it. However typical job postings include company overview; but they said remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Company overview is not those. However they also said "Prefer a few substantive sections over many thin ones." Including company overview would add another section maybe "About the company". But they didn't list that as a heading we must use; they only listed specific headings. So we should not add extra headings beyond those. So we should omit company overview because we don't have a heading for it and we shouldn't create new headings. So we omit. Similarly location list etc omitted. Now ensure we don't include "Learn more at applied.co." etc. Now produce final HTML. Let's craft:

                About the role

                Applied Intuition is building a robot learning platform on Dana, its physical AI platform: the data infrastructure and training intelligence a company needs to make any robot learn an industrial task and keep improving it. The robotics team builds that platform and uses it to deliver robot autonomy on customer lines, training, evaluating, and deploying policies on real robots doing real industrial tasks. Everyone on the team works hands-on with hardware and sees their work reach customers. As