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About This Role
Join ABBYY and be part of a team that celebrates your unique work style. With flexible work options, a supportive team, and rewards that reflect your value, you can focus on what matters most – driving your growth, while fueling ours.
Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing.
As a trusted partner for purpose\-built AI and intelligent automation, we solve highly complex problems for our enterprise customers and put their information to work to transform the way they do business. Over 10,000 customers trust ABBYY, including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as DHL, Johnson \& Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H\&R BLOCK.
### About the role:
ABBYY is investing heavily in artificial intelligence to accelerate innovation across our product portfolio. As a Senior Product Manager, AI, your mission is to define, build, and scale AI\-powered capabilities that drive meaningful customer outcomes and business growth.
This is a highly strategic role where you will own the vision and execution of AI initiatives, working cross\-functionally to bring intelligent, data\-driven products and features to market. You'll partner closely with engineering, data science, and design teams to translate cutting\-edge AI technologies into scalable, customer\-facing solutions.
You will be responsible for shaping the AI product roadmap, identifying opportunities for innovation, and ensuring successful delivery across the full product lifecycle. This role blends strategic thinking, technical depth, and execution rigor, and is critical to ABBYY's continued leadership in intelligent automation.
You will report into Product leadership and collaborate closely with cross\-functional stakeholders across Product, Engineering, and Go\-To\-Market teams.
### Job Responsibilities:
Define and Execute AI Product Strategy
- Partner with product managers and key stakeholders to define and execute the strategic roadmap for AI\-powered products and features, aligning with company objectives and customer needs
- Prioritize investments and initiatives that drive business growth and innovation
Drive AI Innovation and Opportunity Identification
- Lead market analysis, customer research, and competitive assessments to identify opportunities for AI integration and differentiation
- Stay informed on emerging AI technologies to inform product direction
Lead End\-to\-End Product Lifecycle
- Collaborate with engineering, design, data science, and cross\-functional teams to design, develop, and launch AI\-driven solutions
- Own product requirements including user stories, technical specifications, success metrics, and go\-to\-market strategies
Deliver Foundational AI Capabilities
- Own and evolve core AI/ML services leveraged across multiple product lines
- Ensure seamless integration of AI capabilities into enterprise workflows
Champion AI Across the Organization
- Evangelize AI initiatives both internally and externally, clearly articulating value, risks, and opportunities
- Educate stakeholders and leadership on AI strategy and business impact
Drive Data\-Driven Execution
- Leverage experimentation, analytics, and customer insights to continuously improve product performance
- Define and track key metrics such as model accuracy, inference latency, and infrastructure cost optimization
- Operate with a strong focus on measurable outcomes, iteration, and impact
### Job Requirements:
- 5\+ years of product management experience, including strong experience delivering AI/ML\-driven products
- Proven track record of launching successful AI\-enabled products from concept through commercialization
- Strong understanding of machine learning fundamentals, AI applications, and data\-driven product development
- Working knowledge of OCR technologies and Intelligent Document Processing (IDP) systems
- Deep familiarity with multi\-modal models, including Vision Language Models (VLMs) and Large Language Models (LLMs)
- Exceptional analytical and problem\-solving skills with the ability to translate complex data into actionable insights
- Strong communication and stakeholder management skills, with the ability to influence across all levels
- Ability to operate effectively in fast\-paced, ambiguous environments and drive results
### Preferred Qualifications:
- Bachelor's or advanced degree in Computer Science, Engineering, Business, or a related field
- Hands\-on experience partnering closely with machine learning engineers and data scientists
- Familiarity with AI/ML tools, platforms, and product development ecosystems
- Experience with model optimization and compression techniques
- Familiarity with generative AI workflows such as prompt engineering, Retrieval\-Augmented Generation (RAG), or agentic frameworks
### Salary Range:
The anticipated base salary range for this position is between $155,000 and $170,000. Base salary may vary depending on factors such as geographic location, relevant experience, skills, and internal equity. There is no guarantee an offer will be at the top of the posted range.
### Here are some of our local benefits:
- Generous Paid Time Off: Three weeks of PTO, plus 15 paid holidays, floating holidays, and increased holiday allowance based on tenure
- Health Coverage: Medical, dental, and vision insurance
- Retirement Savings: 401(k) plan with up to a 4% employer match
- Wellness Support: Gym membership subsidy
- Family \& Lifestyle Benefits: Pet insurance and a College Savings Plan
- Employee Support: Employee Assistance Program (EAP)
Join ABBYY, and you will:
Love how you work
- We provide remote and hybrid working options to fit all lifestyles.
- We use flexible hours across most of our teams to allow you to find your own definition of balance.
- Encouraging a culture of giving, we provide two paid volunteering days off every year so you can take time to contribute to the causes you care about.
- To ensure your family is cared for, we offer paid parental leave in all our locations.
Love whom you work with
- We are a global team of 600\+ colleagues, spread across 15 countries on four continents.
- With colleagues representing 30\+ nationalities, our workforce reflects the world.
- Innovation and excellence run through our veins. Our teams gather the expertise which has garnered ABBYY more than 140 technology patents.
- We are guided by the values of respect, transparency, and simplicity.
- "Team Environment" is in the top three highest\-scoring drivers of engagement across all of our departments.
Love what you work on
- We are a company with more than 35 years of experience in the technology market;
- Over 10,000 customers trust ABBYY, including many Fortune 500 ones, with names such as DHL, Johnson \& Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H\&R BLOCK;
- We have modernized the capture market by creating the first low\-code/no\-code IDP platform.
- Our Machine Learning, Natural Language Processing, Computer Vision Technologies, and a marketplace built with AI, can transform any document in any process;
- Top Analyst firms recognize ABBYY's market leadership, including Gartner, Everest PEAK Matrix ® Assessment, ISG Intelligent Automation Lens, and NelsonHall, amongst others.
*ABBYY is an Equal Employment Opportunity employer that values the strength that diversity brings to the workplace. To learn more about our commitment to Diversity and Inclusion, check out the careers section on our website.*
Salary Context
This $155K-$170K range is below the median for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At ABBYY, 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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $155K to $170K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ABBYY AI Hiring
ABBYY has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $170K - $170K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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