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About This Role
### About the Role
Join a well\-funded Series A startup building the logistics orchestration and quality compliance platform for cold chain. Every year, billions of dollars of temperature\-sensitive products — vaccines, therapies, and biologics — are lost in transit because planning, logistics, and quality teams operate on disconnected point solutions and make decisions too late. This platform brings every data source together, automates release and qualification decisions, and delivers prescriptive recommendations in real time.
The platform is AI\-native and self\-improving: each shipment sharpens lane models, updates SOPs from reality, and ensures the next decision starts from evidence rather than a blank form. This is a high\-ownership, customer\-facing role with a direct line to the executive team and Fortune 500 customers in pharmaceutical manufacturing and logistics.
### What You'll Do
- Own the product roadmap for core platform modules and a growing roster of AI agents (live today: risk and data summary agents; coming next: lane risk, documentation, and LSP management agents, plus custom agents customers build themselves)
- Define and enforce a clear agentic contract: agents surface context and recommendations with reasoning shown, execute only what users delegate, and log every action for audit
- Spend meaningful time with customers in pharma logistics, QA, and supply chain operations — translating complex, regulated workflows into simple, intuitive product
- Collaborate daily with engineering, design, data science, and ML to deliver AI features grounded in the platform's data and knowledge layers
- Define concrete success metrics (decisions automated, hours returned, releases accelerated) and hold the team accountable to them
- Maintain and refine product requirements, roadmap documentation, and cross\-functional delivery processes using agile methodologies
### What We're Looking For
Required
- 5\+ years in product management (or equivalent technical PM / engineer\-to\-PM background), with at least 2\+ years building AI/ML\-powered B2B SaaS products
- Proven experience collaborating with engineering teams to deliver software features on a roadmap
- Experience creating and maintaining product roadmaps and writing clear product requirements
- Strong cross\-functional collaboration skills across engineering, design, analytics, and QA
- Fluent enough with LLMs, agents, and RAG architectures to hold your own with ML engineers — and disciplined enough to avoid shipping AI for its own sake
- Experience defining and tracking KPIs for shipped features
- Familiarity with agile methodologies (Scrum or Kanban)
- Must be US\-based; no visa sponsorship available
Nice to Have
- Experience in cold chain, pharma logistics, or GxP\-regulated environments
- Comfort working in or leading remote/distributed teams
- Background in supply chain ops, QA workflows, or regulated SaaS products
You Are
- A simplifier: you shrink scope, ship, and iterate
- Customer\-obsessed, with shipped products you're genuinely proud of
- Equally comfortable in strategic roadmap conversations and hands\-on technical discussions with engineers
### Tech Stack
Python, Node.js, React, PostgreSQL (Supabase), Azure Kubernetes, feature flagging, and modern LLM tooling in the development loop.
### Compensation \& Benefits
- Salary: $170,000 – $200,000 USD annually
- Meaningful equity package
- Five weeks PTO
- Full health benefits
### Location
Fully remote — open to candidates based anywhere in the United States. Visa sponsorship is not available.
Salary Context
This $170K-$200K range is below the median 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 CLERA, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $170K to $200K.
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.
CLERA AI Hiring
CLERA has 14 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Engineer, LLM Engineer. Positions span Palo Alto, CA, US, San Francisco, CA, US, New York, NY, US. Compensation range: $150K - $250K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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