Interested in this AI Product Manager role at Spot AI?
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Who we are.
Spot AI turns cameras into coworkers.
We are building the category of AI coworkers for the physical world. Our platform helps businesses understand, automate, and act across their environments in real time.
Spot AI is relied on at over 10,000 locations by more than 1,000 customers across manufacturing, construction, logistics, and retail. We are backed by over $110 million from investors including Scale, Redpoint, Qualcomm Ventures, StepStone, and Bessemer.
We move fast, take ownership, and focus on outcomes. We are building a company for people who want to solve real problems and have a direct impact on how businesses operate.
Ready to learn more? Connect with us on LinkedIn | X | YouTube
We’re looking for a builder to help scale our AI Agents for manufacturing.
Manufacturing represents Spot AI's strongest growth driver. Our AI Ops Assistant and autonomous agents are actively deployed on production lines across major national manufacturers, driving continuous demand for expanded floor level capabilities.
We are seeking a Product Manager to own execution for our manufacturing agents product line and convert this momentum into a scalable enterprise product. In this role, you will identify high impact customer challenges, develop reusable platform capabilities, and transform custom implementations into a scalable business model.
This domain involves complex, unstructured environments. You will partner with operational leaders who manage intricate workflows, navigate high stakes environments, and demand immediate, measurable value from technology.
Successful product managers at Spot AI act as hands on builders. They collaborate closely with engineering to resolve technical tradeoffs, conduct direct plant floor evaluations, and launch products that achieve sustained operational adoption.
Prior domain expertise in manufacturing is not required. We prioritize candidates with a demonstrated track record of building B2B products from ground zero, who are AI native, and have helped scale a product customers are already pulling for.
Your assignment.
Uncover High Value Opportunities
- Engage directly with clients, account executives, sales engineers, channel partners, FDE, and field teams to map out workflows, operational friction, budget parameters, and key success metrics.
- Identify top tier use cases by evaluating genuine strategic value and recommend appropriate priorities
Develop Scalable Platform Core Capabilities
- Transform custom, single client solutions into standardized product features that seamlessly scale across multiple facilities and organizations.
- Recognize core components within each implementation to accelerate future deployments at scale.
Collaborate Closely with Technical Teams
- Partner directly with engineering teams on AI models, evaluation pipelines, system architecture, and iterative prototyping.
- Own technical prioritization for your product area, by deciding what features to develop, postpone, or reject.
Partner on Go-to-Market Strategy & Execution
- Establish an end to end framework spanning discovery, proof of concept testing, full production rollout, and commercial scaling.
- Align with sales, marketing, and customer success leadership on market positioning, pricing models, demonstrations, and ROI case studies to streamline commercial adoption.
Who you are.
Whether you have operated as a founder, product manager, technical product lead, or forward-deployed engineer, what we value most is your proven track record of directly building products and delivering them to real-world users.
Ideal candidates bring the following qualities:
- Entrepreneurial Mindset: Experience as a founder or early stage startup builder who has shipped B2B products from concept to market adoption and revenue growth under resource constraints.
- High Agency & Speed: You move quickly and can clearly demonstrate what you built, scaled, and delivered over the last six months.
- Real World AI Expertise: A strong grasp of AI model behavior in practical deployments, including accuracy trade-offs, false positives, latency, and cost.
- Hands-On Problem Solving: A builder who actively prototypes, analyzes data, and works directly alongside engineering teams instead of depending on static spec sheets.
- Customer & Commercial Orientation: Eager to collaborate closely with sales teams and customers to unearth key insights that drive product decisions.
- Strong Interpersonal & Technical Credibility: Ability to seamlessly navigate conversations ranging from deep technical reviews with engineers to onsite customer meetings.
- Scalable Product Judgment: Sharp instincts for separating unique, isolated customer requests from repeatable, platform-level features.
- Clear & Impactful Communication: Ability to convert ambiguity into crisp product requirements, go to market strategies, and compelling business models.
- A background in computer vision, video AI, industrial automation, robotics, or forward deployed engineering is a strong plus.
Note that this is an IN PERSON role at our office located at 650 5th St. (near Oracle Park) in San Francisco. There will also be occasional travel to customer sites.
Unfortunately we are not able to offer relocation assistance or visa sponsorship for this opportunity.
You could be an ideal fit if you:
- Are eager to expand and scale a product customers are actively pulling for.
- Thrive in ambiguous environments and excel at spotting underlying trends within custom client requirements.
- Are equally passionate about partnering with engineering teams and spending time onsite with customers.
- Can articulate how you will help shape the product experience and platform capabilities for manufacturing customers.
This role is probably not a good fit if you:
- Favor following a pre-established roadmap over uncovering and defining new product directions.
- Prefer authoring strategy documents rather than actively building and launching features.
- Feel more at home in a hierarchical product management structure than taking an entrepreneurial, founder-like approach.
- Are unable or unwilling to work on site from our San Francisco office location.
Compensation and Benefits.
- Target Annual Base Salary Offer Range: $195,000 - $210,000 in the San Francisco Bay Area. The offered salary will depend on the offered candidate’s demonstrated skills and competencies for the role.
- Meaningful early stage equity.
- Medical, dental, and vision plan options with little to no cost for employees and dependents.
- Company paid short and long term disability plans.
- Company paid life insurance.
- 401K with employer match.
If you want to define a new category of AI for the physical world, and build the product that makes every deployment faster than the last, we should talk.
The full annual base salary range for this position is $195,000 - $225,000. We hire within the target range so every new Spotan has meaningful room to grow their pay over time.
We are creating and cultivating a diverse and inclusive culture where we celebrate individuals for what they accomplish, no matter who they are. As an equal opportunity employer, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Salary Context
This $195K-$225K range is above the median for AI Product Manager roles in our dataset (median: $193K across 93 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Spot AI, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($210K) sits 5% below the category median. Disclosed range: $195K to $225K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Spot AI AI Hiring
Spot AI has 1 open AI role right now. They're hiring across AI Product Manager. Based in San Francisco, CA, US. Compensation range: $225K - $225K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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