[All remote jobs](https://jobicy.com/jobs.md)[![Homebound logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/04/Homebound.jpg)](https://jobicy.com/company/homebound.md)[Homebound](https://jobicy.com/company/homebound.md) # Staff Product Manager, Physical AI Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/homebound.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 170k–200k / yrDepartment[Product & Operations](https://jobicy.com/categories/management.md)EmploymentFull TimeExperienceDirectorPublished2 Oct 2026Apply before1 Nov 2026Listing views39Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Homebound seeks a Staff Product Manager to own strategy, roadmap, delivery, and adoption for Physical AI products used on active residential construction sites. The role covers mobile field workflows, onsite data capture through drones, 360-degree walkthroughs and phones, and predictive construction scheduling. This leader will translate computer vision, multimodal models, and AI agents into reliable, safety-conscious products that perform under noisy real-world conditions and intermittent connectivity. The position requires staff-level technical product ownership, strong AI evaluation practices, and independent cross-functional leadership with Construction Operations, Field Operations, and Engineering. Success depends on improving model accuracy, field adoption, and operational efficiency through rigorous requirements, experiments, human review, and feedback loops. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a highly complex staff-level product role because it operationalizes frontier AI in variable, safety- and cost-sensitive physical environments rather than controlled digital workflows. The PM must make technical tradeoffs across multimodal AI, on-device inference, evaluation quality, field usability, and cross-functional adoption with limited playbooks. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$185,000US market range$170k–$230k0$253k AI insightThe disclosed yearly base-salary range is $170,000-$200,000 USD, with a midpoint of $185,000. For a US-based Staff Product Manager specializing in applied AI and physical-world products, an estimated broader market base-salary range is approximately $170,000-$230,000 annually; total compensation may be higher depending on equity and other variable compensation, which are not quantified in the posting. ## Core skills Skills and capabilities most closely associated with this opportunity. [Product strategy](https://jobicy.com/jobs?search_keywords=Product%20strategy.md)[Applied AI](https://jobicy.com/jobs?search_keywords=Applied%20AI.md)[Computer vision](https://jobicy.com/jobs?search_keywords=Computer%20vision.md)[Multimodal AI](https://jobicy.com/jobs?search_keywords=Multimodal%20AI.md)[LLM agents](https://jobicy.com/jobs?search_keywords=LLM%20agents.md)[Mobile products](https://jobicy.com/jobs?search_keywords=Mobile%20products.md)[On-device inference](https://jobicy.com/jobs?search_keywords=On-device%20inference.md)[AI evaluation](https://jobicy.com/jobs?search_keywords=AI%20evaluation.md)[Human-in-the-loop systems](https://jobicy.com/jobs?search_keywords=Human-in-the-loop%20systems.md)[Construction technology](https://jobicy.com/jobs?search_keywords=Construction%20technology.md) Sample interview questionsDescribe an applied AI product you took from concept to production. What decision or action did it enable, and how did you measure success?I would describe the user problem, the model and workflow constraints, and the phased launch plan. I would quantify outcomes such as precision and recall, task-completion time, adoption, override rate, and downstream operational impact, while explaining how feedback informed subsequent iterations. How would you establish a quality bar for a computer-vision feature that detects construction-site issues from photos? I would create a representative evaluation set covering lighting, site stages, devices, trade types, and failure cases. I would set class-specific precision and recall thresholds based on the cost of false positives and false negatives, validate performance in field pilots, and include human review or escalation where model confidence is insufficient. How would you design an AI workflow for superintendents who have limited time, unreliable connectivity, and gloves on? I would start with field observation and simplify the workflow to fast capture and clear, actionable outputs. The product would support offline-first data capture, on-device or deferred inference where needed, minimal typing, confidence-aware recommendations, and a lightweight correction flow that contributes labeled feedback. How do you decide whether an emerging AI capability is ready for deployment in a high-consequence operational setting? I assess the capability against the actual task distribution, error costs, latency, privacy, reliability, integration complexity, and available human safeguards. I would use controlled pilots and predefined go/no-go criteria, deploying only where the model's value exceeds its operational risk and where failure modes are observable and recoverable. Tell us about a time you aligned technical and nontechnical stakeholders around an ambiguous product direction. I would explain how I translated a shared problem into explicit user outcomes, constraints, decision points, and measurable success metrics. I would use prototypes, data, and regular cross-functional reviews to surface tradeoffs early, make ownership clear, and maintain alignment through launch and iteration. Opportunity details ## About this role. About Us: Homebound is on a mission to make it possible for anyone, anywhere, to build a home using technology. Created by an experienced team of construction, real estate, design, and technology experts, Homebound is transforming the residential construction industry by improving the costly and inefficient process of building a home. We’ve created an entirely new way to build homes with technology powering every stage from start to finish to provide a seamless experience for our customers. Homeowners across the country can choose where they want to live, select a home plan that’s perfect for them, then personalize and buy it, all online. Homebound has raised $150M in capital from leading venture capitalists like Google, Khosla, Thrive Ventures, and we’re scaling quickly in places like Texas, Colorado and Florida. Come build your future with us. ### About the Role: As Staff Product Manager, Physical AI, you’ll own the technology that brings AI to the construction site. This is where the digital and physical worlds meet and where most AI products quietly fall apart. Your product surface spans our mobile field app, used by every superintendent, onsite capture including drones, 360° walkthroughs, phone-based capture, and construction schedules that predict slip before it happens and explain why. You don’t need a construction background. You do need to have taken frontier models out of the browser and made them work somewhere messy where inputs are noisy, conditions change daily, and a wrong answer costs a real person a trip to the site. ### Why This Is a Genuinely Hard Problem: Every home we build generates thousands of photos, plan revisions, inspection results, and schedule events. We have years of structured build data tied to a single shared data platform – longitudinal, real-world construction information that is very difficult to assemble from outside the industry. The hard part isn’t the model. It’s that a jobsite is dusty, half-built, poorly lit, and different every day. Precision matters asymmetrically: a false positive sends a trade partner on a wasted truck roll. Connectivity is unreliable, so some inference has to run on-device. And the people using what you build are on their feet in the sun, wearing gloves, with three minutes to spare. ### What You’ll Do: * Own the field AI strategy and roadmap. Set the vision and execute end to end across the entire field portfolio – concept through rollout and adoption. * Ship AI into the physical environment. Translate computer vision, VLMs, and agents into products that hold up on an active jobsite, not just in a demo. * Build the quality bar. Establish eval sets and quality frameworks for AI features, design the human-in-the-loop review that makes them safe to ship, and close the flywheel so field corrections make the models better. * Set the direction. Bring a strong, well-formed perspective on where field technology is going, defend it, and use it to shape how Homebound applies new capability to the teams building our homes. * Drive across teams. Move initiatives forward across Construction Operations, Field Operations, and Engineering – independently, without waiting for a playbook. * Define and measure with rigor. Write clear, actionable requirements; prioritize on impact, effort, and durable value; define success metrics and run experiments that systematically improve accuracy, adoption, and field efficiency. ### What You’ll Bring: * 8+ years of product management, including end-to-end ownership of a complex, technical product at staff-level scope. * A track record of shipping applied AI – computer vision, multimodal, LLM/agent-based, or ML systems – that took meaningful action or made decisions in production. Not chat wrappers, not lightweight integrations. * Technical fluency to operate as a peer to engineers and ML teams: reasoning through system tradeoffs, reading an eval and knowing whether it’s measuring the right thing, and going into the data to answer your own questions. * A real point of view on emerging AI. You follow the frontier closely, you have an opinion about what’s ready to deploy and what isn’t, and you can defend it. * 0 to 1 experience taking a product from concept through launch to real-world adoption, moving fast in ambiguity without heavy process. * Communication that translates cleanly between a superintendent standing on a jobsite, an ML engineer, and the executive team. Our Commitment: We are focused on building a diverse and inclusive workforce. If you’re excited about this role, but do not meet 100% of the qualifications listed above, we encourage you to apply. To apply, please submit an application with your resume on the Career’s page. Homebound is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, state, or local law. Homebound considers all qualified applicants in accordance with the San Francisco Fair Chance Ordinance. Our Compensation Philosophy: Our salary ranges are determined by role, level, and location. Please note that the salary range displayed on each job posting may vary by state. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter will share more about the specific salary range for your preferred location during the hiring process. Please note that each job posting includes a general description of any other compensation offered for the position in addition to the salary range displayed on the job posting. You can find information about our benefits [here](https://drive.google.com/file/d/1B9WSBBE-fDRH0jSaDX2zsfMfxQSe5DqE/view?usp=drive_link). 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