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About This Role
For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well.
For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you.
Angi at a glance:
- Founded in 1995 as Angie’s List and rebranded in 2021
- Global company with 9 brands in 8 countries and employees worldwide
- Homeowners have turned to us for 300 million home projects and counting
About the team
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This Principal Technical Product Manager will own the vision, strategy, and roadmap for the AI Platform: the shared, governed entry point every model and agent workload at Angi runs through. As generative AI moves from experiment to production across the company, this team manages the common platform with routing, cost attribution, quality evaluation, and guardrails built in rather than rebuilt by every team.
The ideal candidate is a technical product manager who treats infrastructure as a product and has a strong thesis on how AI is reshaping the way software and models get built and served. You will own the platform that our product engineering, data science and their associated agents depend on: a model\-agnostic AI Gateway, eval\-gated prompt management, self\-hosted and fine\-tuned model serving, and the trust\-and\-autonomy ladder that governs how much independence each agent earns. Your measure of success is not just adoption and scale, but are we meaningfully improving our products and enabling our internal teams.
What you'll do
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Product Management
- AI Gateway \& Cost Governance: Own the vision for a governed, model\-agnostic gateway that all model and agent traffic routes through, with per\-team and per\-use\-case cost attribution, flexible model routing, rate limiting, provider failover, and automatic spending caps and stop switches. Make model\-swap and cost decisions changeable once at the gateway, not per service.
- Evaluation \& Quality Enforcement: Define the roadmap for the eval engine and the pass/fail gate that runs on it, eval\-gated prompt management with versioning and rollback, and per\-agent accuracy scoring — so quality regressions are caught before they reach users rather than surfacing downstream in business metrics.
- Trust \& Autonomy Ladder: Define and champion the trust\-and\-autonomy ladder — the thresholds, progression criteria, evidence, approvals, and rollback logic that govern when an agent earns more independence — so agentic adoption scales with accountability instead of governance gaps.
- LLM \& ML Serving Infrastructure: Own the production path self\-hosted LLM /open\-weight model serving and traditional ML with the goal to consolidate both ML and LLM workloads under one standardized observable platform.
- Cost \& ROI Telemetry: Define and instrument the metrics that track the platform health as well as business impact to the platform.
Execution And Leadership
- Cross\-Functional Partnership: Establish deep partnerships with product engineering, architecture and data science to drive adoption and make sure the platform reflects how teams actually build and serve models.
- Technical Roadmap Management: Manage a complex platform backlog spanning parallel tracks and under real capacity constraints. Make and communicate authoritative sequencing and trade\-off decisions with concise and clear communications.
- Stakeholder Communication: Act as the primary interface between technical platform teams and business stakeholders, translating infrastructure investment into clear business outcomes
Who you are
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Minimum Qualifications
- 8\+ years of experience in Product Management, with at least 4 years focused on infrastructure, platforms, ML/AI systems, or other technical products serving internal engineering or data\-science customers.
- Proven track record scaling technical platforms from inception through maturity for demanding internal customers.
- Technical fluency across the modern AI/ML stack — model serving and inference, API gateways/proxies, evaluation and testing frameworks, and cloud/Kubernetes infrastructure — sufficient to “swim with the fishes” with engineers and data scientists on architecture trade\-offs.
- Demonstrated ability to use telemetry and cost data (model performance, eval results, adoption analytics, spend data) to diagnose bottlenecks and drive product strategy.
Preferred Qualifications
- Hands\-on familiarity with LLM gateways/proxies (e.g., LiteLLM), evaluation and observability tooling (e.g., Langfuse), and model serving frameworks (e.g., KServe, SageMaker, Bedrock, Ray).
- Experience with prompt management and versioning, LLM fine\-tuning, or self\-hosted/open\-weight model serving in production.
- Familiarity with agent governance concepts — guardrails, PII controls, prompt\-injection protection, and autonomy/trust frameworks — and with the tension between agent velocity and cost control.
- Experience operating a platform across competing tracks and stakeholders, sequencing revenue\-critical stabilization work alongside a new strategic build\-out.
- Exceptional leadership skills with a history of influencing cross\-functional teams without direct authority.
Compensation \& Benefits
- The salary band for this position ranges from $190,000 – $280,000 commensurate with experience and performance. Compensation may vary based on factors such as geographic location.
- This position will be eligible for a competitive year end performance bonus \& equity package.
- Full medical, dental, vision package to fit your needs
- Flexible vacation policy; work hard and take time when you need it
- Pet discount plans \& retirement plan with company match (401K)
- The rare opportunity to work with sharp, motivated teammates solving some of the most unique challenges and changing the world
We value diversity. We know that the best ideas come from teams where diverse points of view uncover new solutions to hard problems. We welcome and value individuals who bring diverse life experiences, educational backgrounds, cultures, and work experiences.
*Our hiring process may utilize artificial intelligence (AI) tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision\-making.*
\#LI\-Remote
Salary Context
This $190K-$280K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $185K across 167 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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Angi, 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 Required
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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 8% above the category median. Disclosed range: $190K to $280K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Angi AI Hiring
Angi has 1 open AI role right now. They're hiring across AI Product Manager. Based in Denver, CO, US. Compensation range: $280K - $280K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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