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About This Role
Job Description
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Position Summary:
We are looking for a Senior Product Manager who leads with product instinct, operates with AI tools as core infrastructure, and ships products that help our customers and our business achieve their goals.
This role is built for a product manager with genuine AI fluency who uses it to operate at a different level — compressing research cycles, accelerating decisions, and raising the quality of everything the team produces. You will own a product area end\-to\-end, from market and customer understanding through roadmap delivery and cross\-functional alignment, working closely with engineering, design, customer success, and go\-to\-market teams to make sure what we build solves real problems and delivers measurable value. Your work will help our customers reach their goals and drive Momentive’s growth forward.
This position reports to the Director of Product Management. This is a remote, US\-based position.
Key Responsibilities
- Develop a deep understanding of the market, competitive dynamics, and emerging trends — and engage directly with users and buyers through interviews, data analysis, and feedback loops to uncover pain points, use cases, and unmet needs.
- Own and evolve a 6–12 month roadmap for your product area — managing dependencies, tracking delivery, and adjusting as priorities shift — while keeping a clear line of sight to whether delivery translates into customer adoption, retention, and revenue.
- Translate stakeholder input and customer needs into structured initiatives and epics, ensuring clarity and alignment across teams.
- Define and own the KPIs that measure product health and validate whether released features actually deliver value — then use what you learn to drive the next cycle
- Use AI tools as core tools in your daily work — not as novelties, but as force multipliers for research, analysis, communication, and decision\-making:
- Compress discovery and validation cycles by using AI to synthesize customer feedback, analyze competitive landscapes, dig through large datasets and interviews, and generate first\-draft artifacts (PRDs, specs, user stories) that you then refine with product judgment.
- Use AI for rapid prototyping: build working prototypes to put in front of customers and validate direction early and often.
- Arrive at planning discussions with working analyses and a clear point of view — not just slide decks.
- Act as a central point of communication between Engineering and business\-facing teams (Sales, Marketing, Customer Success), and partner with senior leadership to define and communicate your product area’s vision and positioning.
- Champion innovation within your product area — identifying opportunities for differentiation and running experiments that test new ideas quickly.
- Manage Product Owners and mentor Product Managers: provide direction, coaching, and feedback to ensure effective backlog management, well\-crafted user stories, and strong execution that reflects strategic intent and customer value. Step in to absorb Product Owner duties when needed.
- Contribute to the continuous improvement of product development practices, tools, and collaboration methods across the Product organization.
Qualifications:
- Bachelor’s degree or equivalent work experience; 5\+ years in a software/SaaS environment, including 4\+ years in a product management role with go\-to\-market experience.
- Demonstrated, meaningful use of AI tools in your own PM workflow. You should be able to speak concretely to how AI has changed the way you work, what it’s replaced, and where you’ve found its limits.
- Proven success driving product strategy on a 6–12 month horizon that delivers measurable customer and business outcomes — grounded in strong market analysis and an understanding of product packaging and pricing.
- Proven experience mentoring or guiding Product Owners, Business Analysts, or other product team members, and leading through influence to build strong cross\-functional partnerships.
- Proven success working in Agile environments, including driving process improvement and tracking metrics for optimal team output.
- Revenue fluency and strong business acumen: able to read customer signals as product priorities with business outcomes attached — including churn risk, deal blockers, and expansion opportunities — and comfortable presenting to executives and external partners.
- Advanced understanding of networks, databases, APIs, cloud infrastructure, and Agile practices — enough to reason about how technical choices affect product decisions.
- Strong experience with Jira; comfort with AI\-assisted prototyping and development tools (e.g., Claude, Cursor, or similar) to validate concepts and accelerate decision\-making.
- Experience working with Associations and/or Nonprofits is a plus.
Ideal Candidate Attributes:
- Customer\-Obsessed: Prioritizes customer needs and continuously works to understand and solve real problems.
- Strategic Thinker: Zooms out to see the big\-picture vision while managing the details that drive execution; uses data to guide decisions and trusts judgment when the data runs out.
- Influential Leader: Aligns and inspires cross\-functional teams and stakeholders, building partnerships and driving momentum across teams and time zones.
- Outcome\-Focused: Draws a clear line from product decisions to customer success and business results, and treats shipping as the start of accountability, not the end.
- AI\-Native: Treats AI as a core part of the job, not a novelty. Uses it daily for research, synthesis, prototyping, and communication, and shares what works with the team.
About Us
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Momentive Software amplifies the impact of over 20,000 purpose\-driven organizations in over 30 countries, with over $11 billion raised and 55 million members served to date. Mission\-driven nonprofits and associations rely on Momentive’s cloud\-based software and services to address their most pressing challenges – from engaging their communities to simplifying operations and growing revenue. Designed to help organizations connect more, manage more, and ultimately expect more, Momentive's solutions are built with reliability at the core and strategically focus on fundraising, learning, events, careers, volunteering, accounting, and association management. Momentive partners with organizations that believe "good enough" is never enough – so they can bring on better outcomes for everyone they serve. Learn more at momentivesoftware.com .
Why Work Here?
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At Momentive Software, we’re a team of passionate problem\-solvers, innovators, and volunteers who believe in using technology to make a real difference. We dream big, support each other, and take pride in creating solutions that help our customers drive meaningful change. If you’re looking for a place where your work matters and your ideas are valued, you’ll find it here.
Medical, Dental \& Vision Benefits
401(k) Savings Plan with Company Match
Flexible Planned Paid Time Off
Generous Sick Leave
Inclusive \& Welcoming Environment
Purpose\-Driven Culture
Work\-Life Balance
Commitment to Community Involvement
Employer\-Paid Parental Leave
Employer\-Paid Short\-Term Disability
Remote Work Flexibility
Momentive Software actively embraces diversity and equal opportunity in a meaningful way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be, which is why we do not discriminate based on race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
All persons hired will be required to verify identity, minimum age of 18, eligibility to work in the United States (without sponsorship), and to complete the required employment eligibility verification form upon hire.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Momentive Software, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Momentive Software AI Hiring
Momentive Software has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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