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APPLICATION DEADLINE: We encourage you to apply soon if interested, this role will be taken offline based on applicant volume.
We recognize that job searching can sometimes feel uncertain, and we want to be respectful of your time and interest. We're committed to providing updates after the review period and thank you in advance for considering M\-Files as your next career opportunity
Who We Are
M\-Files is redefining how work gets done. Our context\-first document management system offers purpose\-built business use cases—spanning universal and industry\-specific workflows—to enable secure collaboration, automate processes, and ensure governance.
Unlike traditional systems, M\-Files organizes content around the context of your business, connecting documents to related people, projects, and transactions. With our unique metadata\-driven architecture, organizations can model content in line with their business processes, unify information across silos, and apply AI at scale. The result is greater productivity, reduced risk, and smarter, faster decisions for over 6,000 customers in 100\+ countries.
At M\-Files, our Guiding Principles unite us across diverse cultures and personalities:
- Make It Happen – We set bold goals, take ownership, learn from mistakes, and relentlessly pursue results.
- Help Others – We lead with kindness, assume good intentions, hold one another accountable, and celebrate wins together.
- Love Customers – We put customers and partners at the heart of everything, delivering value with respect, fairness, and speed.
To learn more about us we encourage you to visit our company page.
To learn more about how we became a Certified Great Place to Work visit, Working at M\-Files \| Great Place to Work.
Summary of the role:
We are seeking a strategic, passionate, and execution\-oriented Senior Product Marketing Manager to lead platform product marketing for the M\-Files Context\-First Document Management platform. This role owns positioning, messaging, go\-to\-market strategy, and market leadership for our AI, cloud, and core platform capabilities.
This is a highly cross\-functional role requiring close collaboration with Product Management, Industry Marketing, Sales, Customer Success, Enablement, and Partner teams.
Key Responsibilities
Platform Strategy \& Positioning
- Own and refine the overarching platform story, with special emphasis on context\-first document management, AI and agentic automations, scalability, security, and extensibility.
- Define differentiated messaging and value propositions for platform capabilities across personas and industries.
- Ensure consistency and clarity of platform\-level messaging across all marketing and sales touchpoints.
Go\-to\-Market Execution (Market Launches, Content, Campaign Support)
- Lead platform feature releases, orchestrating cross\-functional launch strategies for AI, cloud, and core platform enhancements.
- Partner with Product Management to influence roadmap decisions based on market opportunity, customer needs, and competitive insights.
- Drive content strategy for platform launches, including pitch decks, solution briefs, whitepapers, demos, customer stories, webpages, and sales tools.
Market Intelligence \& Competitive Analysis
- Own competitive and market intelligence for platform\-level capabilities, especially in AI\-driven information management and cloud content services.
- Identify market trends, customer pain points, and emerging competitors to guide product and GTM strategy.
- Deliver actionable insights to Sales, Product, and Executive teams.
Cross\-Functional Enablement
- Partner with Sales Enablement to ensure clear understanding of platform capabilities, differentiation, and value drivers.
- Support customer\-facing teams with platform\-level messaging guides, battlecards, objection handling, and training content.
Thought Leadership
- Collaborate with the cross\-functional teams to position M\-Files as an industry leader in AI\-powered information management.
- Represent M\-Files in webinars, events, analyst briefings, and customer engagements as a platform subject\-matter expert.
Requirements Required Qualifications
- 8\+ years of product marketing experience, including at least 3 years in a leadership role.
- Demonstrated success in marketing platform technologies, SaaS, AI, or cloud\-based enterprise solutions.
- Strong understanding of enterprise buyer personas, value\-based messaging, and B2B SaaS go\-to\-market motions.
- Proven experience managing successful cross\-functional launches.
- Excellent communication, storytelling, and executive presentation skills.
- Experience managing or mentoring team members.
Preferred
- Experience in content management, information management, workflow automation, or knowledge management markets.
- Background working closely with Product Management teams in a fast\-moving technical environment.
- Experience supporting global sales and partner ecosystems.
Success in This Role Looks Like
- A unified, compelling platform narrative that clearly differentiates M\-Files in the AI and cloud space.
- Strong GTM execution for platform and AI releases, leading to measurable impact on pipeline, adoption, and competitive wins.
- Sales and partners consistently using platform messaging and tools with confidence.
- A highly aligned and effective product marketing function across platform and UX.
- Recognition of M\-Files as a leading innovator in AI\-powered information and knowledge management.
Participation in our Recruitment Process:
- Initial Phone Screen w/People \& Culture Team Member
- Hiring Manager \- VP, Product Marketing
- Internal Team Interview/Stakeholder(s)
- Chief Marketing Officer
- Total Recruitment Process Time Investment for Applicant: Approx. 3hrs
Benefits Why M\-Files?
We are a global company with Finnish roots and a strong passion for delivering innovative solutions that transform industries. By joining M\-Files, you will have the opportunity to shape the future of knowledge work automation while growing your expertise in a collaborative and supportive environment.
Our guiding principles of "Make It Happen", "Help Others", and "Love Customers" are highlighted through our daily actions as a team. Transparent communication and outstanding team spirit were listed as our strengths in our M\-Filer Experience survey.
What We Offer:
- As a remote enabled company our employees enjoy the flexibility to establish their own life/work balance
- 10 paid holidays annually
- Unlimited PTO
- Matching 401K Plan (25% of employees' contribution up to the IRS max)
- Health insurance (PPO and HDHP/HSA plans offered)
- Dental insurance
- Vision insurance
- Life insurance (1x employee salary)
- Short\-term disability (employer paid)
- Long\-term disability (employer paid)
- Flexible Spending Plan (medical and dependent)
Salary Context
This $130K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 M-Files, 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 in Demand for This Role
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. This role's midpoint ($135K) sits 38% below the category median. Disclosed range: $130K to $140K.
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.
M-Files AI Hiring
M-Files has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $140K - $140K.
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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