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Company Description
Guardant Health is a leading precision oncology company focused on guarding wellness and giving every person more time free from cancer. Founded in 2012, Guardant® is transforming patient care and accelerating new cancer therapies by providing critical insights into what drives disease through its advanced blood and tissue tests, real\-world data and AI analytics. Guardant tests help improve outcomes across all stages of care, including screening to find cancer early, monitoring for recurrence in early\-stage cancer, and treatment selection for patients with advanced cancer. For more information, visit guardanthealth.com and follow the company on LinkedIn , X (Twitter) and Facebook .
Position Summary:
The Senior Product Manager, AI will lead the delivery and evolution of Guardant’s AI product roadmap across both internal operations and customer\-facing experiences. This role is responsible for driving conversational, generative, agentic, and predictive AI solutions that improve operational efficiency, scale service capacity, and elevate provider and patient engagement through intuitive, seamless, and cohesive customer experiences.
Partnering with a diverse cross\-functional team, the Senior PM will ensure AI investments align with Guardant’s strategic priorities while delivering end\-to\-end experiences that are consistent, trusted, and user\-centric across every touchpoint. This individual will play a key role in defining and optimizing CX flows, ensuring AI capabilities are thoughtfully integrated into workflows to reduce friction, simplify complex interactions, and create meaningful experiences for providers, patients, and internal teams alike.
The ideal candidate brings deep knowledge of AI technologies, proven experience translating business and user needs into scalable product solutions, and a strong track record of driving cross\-functional alignment across product, design, engineering, operations, and customer\-facing teams. Success in this role requires both strategic thinking and strong execution, with the ability to influence stakeholders and champion a high\-quality customer experience throughout the product lifecycle.
Essential Duties and Responsibilities:
### Product Roadmap
- Partner with stakeholders and Engineering to define and execute the roadmap for Screening AI solutions.
- Prioritize high\-impact use cases that scale operations, improve provider/patient experiences, and align with Guardant’s strategic objectives.
- Stay ahead of AI and healthcare technology trends to ensure competitive and compliant solutions.
### Product Delivery
- Lead the full product lifecycle from ideation through launch, adoption, and iteration.
- Translate business needs into clear product requirements and partner with Engineering to deliver solutions.
- Collaborate with cross\-functional teams to deploy customer\-facing AI solutions that streamline communication, create engaging content, and personalize outreach.
- Define and optimize end\-to\-end customer experience (CX) flows across AI\-powered touchpoints, ensuring interactions are intuitive, cohesive, and aligned with provider and patient needs.
- Identify friction points, improve user journeys, and drive measurable improvements in engagement, satisfaction, and service efficiency.
- Partner with Operations to automate workflows, integrate AI into the CRM and support tools, and enable faster, more accurate decision\-making.
- Partner with Compliance and Legal to ensure AI solutions meet regulatory, privacy, and ethical standards.
### Measurement \& Stakeholder Engagement
- Define and track success metrics that demonstrate efficiency gains, customer impact, and scalability.
- Communicate program updates, trade\-offs, and outcomes to executive stakeholders with clarity and influence.
Qualifications
- 7\+ years of product management experience, with 2\+ in AI/ML\-driven products .
- Strong knowledge of conversational, generative, agentic, and predictive AI applications.
- Proven success delivering AI\-enabled solutions in healthcare, diagnostics, or other regulated industries.
- Deep understanding of healthcare workflows across providers, patients, and operations.
- Exceptional analytical skills, with the ability to tie AI initiatives to measurable outcomes (efficiency, scale, CX impact).
- Excellent communication and stakeholder management skills, including executive presentations.
Bachelor’s degree in computer science, engineering, business, or related field.
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Hybrid Work Model: This section is applicable to onsite employees who are eligible for hybrid work location as specified by management and related policies. Guardant has defined days for in\-person/onsite collaboration and work\-from\-home days for individual\-focused time. All U.S. employees who live within 50 miles of a Guardant facility will be required to be onsite on Mondays, Tuesdays, and Thursdays. We have found aligning our scheduled in\-office days allows our teams to do the best work and creates the focused thinking time our innovative work requires. At Guardant, our work model has created flexibility for better work\-life balance while keeping teams connected to advance our science for our patients.
The annualized base salary ranges for the primary location and any additional locations are listed below. This range does not include benefits or, if applicable, bonus, commission, or equity. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, education, job\-related skills, job duties, and business need. Primary Location: Palo Alto, CA Primary Location Base Pay Range: $188,200 \- $258,800 Other US Location(s) Base Pay Range: $159,970 \- $219,980 If the role is performed in Colorado, the pay range for this job is: $169,380 \- $232,920
*Employee may be required to lift routine office supplies and use office* *equipment. Majority* *of the work is performed in a desk/office environment; however, there may be exposure to high noise levels, fumes, and biohazard material in the laboratory* *environment. Ability* *to sit for extended periods of time.*
*Guardant Health is committed to providing reasonable accommodations in our hiring processes for candidates with disabilities, long\-term conditions, mental health conditions, or sincerely held religious beliefs. If you need support, please reach out to* *[email protected]*
*A background screening including criminal history is required for this role. GH will consider qualified applicants with criminal arrest or conviction histories in a manner consistent with applicable law including but not limited to the LA County Fair Chance Policies and the Fair Chance Act (Gov. Code Section 12952\).*
*Guardant Health is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.*
*All your information will be kept confidential according to EEO guidelines.*
*To learn more about the information collected when you apply for a position at Guardant Health, Inc. and how it is used, please review our* *Privacy Notice for Job Applicants* *.*
*Please visit our career page at:* *http://www.guardanthealth.com/jobs/*
Salary Context
This $159K-$258K range is above the median for AI Product Manager roles in our dataset (median: $187K across 164 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 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Guardant Health, 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 $213,800 based on 610 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $159K to $258K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Guardant Health AI Hiring
Guardant Health has 1 open AI role right now. They're hiring across AI Product Manager. Based in Palo Alto, CA, US. Compensation range: $258K - $258K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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