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About This Role
APPLICATION WINDOW: We encourage you to apply if interested. We will review applications on a rolling basis and keep candidates informed on next steps.
We recognize that job searching can sometimes feel uncertain. As we engage with candidates and continue to evaluate our needs, this role may evolve to ensure strong alignment between the opportunity, the market, and long\-term success.
We are committed to a thoughtful and transparent process and will keep candidates informed along the way. Thank you for considering M\-Files as your next career move.
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:
1\. Make It Happen – We set bold goals, take ownership, learn from mistakes, and relentlessly pursue results.
2\. Help Others – We lead with kindness, assume good intentions, hold one another accountable, and celebrate wins together.
3\. 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
M\-Files is building a company\-wide capability to operationalize AI—turning promising LLM prototypes into secure, reliable, maintainable internal tools that materially improve how teams work. This role sits in Strategic Operations and functions as a hands\-on builder: you will partner with business leaders to identify high\-value workflows that can be accelerated with AI, help reshape the underlying processes, and then build, deploy, and run the AI\-enabled solutions end\-to\-end.
This is not a traditional product software engineer role. Instead, it is an execution\-oriented engineering role embedded in the business: you’ll move quickly from problem definition to production, while applying the right risk controls, oversight, security, and compliance expectations required for internal systems at scale.
You will leverage AI\-assisted development tools as part of your daily workflow to accelerate delivery while maintaining quality.
What You Will Be Doing / Responsibilities and Duties
Discover \& shape high\-value AI opportunities (internal process focus)
- Partner with functional leaders to identify workflows where AI can remove friction, reduce cycle time, improve accuracy, or strengthen compliance.
- Map the current state process, identify bottlenecks and failure modes, and re\-design the process to be automation\-ready (clarify inputs/outputs, decision points, data sources, controls, and ownership).
- Define success metrics (time saved, error reduction, throughput, adoption, auditability) and translate business goals into a build plan.
Build internal AI agents and automation tools (end\-to\-end ownership)
- Design and implement internal agents using modern LLM patterns (tool use/function calling, retrieval\-augmented generation where needed, structured outputs, and human\-in\-the\-loop checkpoints).
- Build whole\-product solutions: lightweight UX, service/API layer, integrations, data access, and automation triggers—appropriate to the use case.
- Use AI\-assisted development techniques to speed delivery while sustaining maintainability and readability.
Operate, mantain, and scale (production mindset)
- Own reliability: monitoring, alerting, logging, incident response, and continuous improvements.
- Establish repeatable patterns for onboarding new workflows and scaling existing ones (templates, shared components, evaluation harnesses, documentation).
- Create and maintain runbooks and lightweight training so internal teams can adopt solutions confidently.
Risk, control, oversight, security \& compliance by design
- Implement appropriate guardrails: data minimization, access controls, secrets management, safe prompt/tooling patterns, output validation, and traceability.
- Ensure solutions meet internal security and compliance expectations (including audit readiness, change management discipline, and clear ownership).
- Maintain clear documentation of how systems work, what data they touch, and how risks are mitigated.
Cross\-functional coordination
- Coordinate across IT/Security, Legal/Privacy, and functional SMEs to get solutions approved and adopted.
- Communicate progress with crisp updates; manage tradeoffs between speed and rigor.
Outcomes to be achieved
- A portfolio of high\-impact internal AI agents deployed into real workflows (not demos), with measurable business outcomes.
- A scalable operating model for internal AI: reusable components, clear governance, and a predictable path from idea production.
- Reduced process friction through AI \+ process redesign, not AI bolted onto broken workflows.
- High trust in outputs through appropriate controls, auditability, and operational reliability.
Working style / What success looks like here
- You are low ego, high output: you can operate independently, but you collaborate naturally and bring others along.
- You can move fast without being reckless: you know where to be scrappy and where to add rigor.
- You care about outcomes: automation only matters if it changes how people work.
Requirements Qualifications
- Demonstrated ability to build and maintain end\-to\-end software (design build deploy operate), with strong engineering fundamentals.
- Proficiency in at least one modern programming language (e.g., Python, TypeScript, C\#, Node.js) and comfort learning what’s needed.
- Practical experience integrating systems via APIs, authentication, and structured data formats.
- Strong ability to work with non\-technical stakeholders: translate ambiguous problems into clear specs, iterate quickly, and drive adoption.
Technical foundations we value (examples)
- Experience building cloud\-based services and the surrounding engineering hygiene (CI/CD, source control, test automation, and operational monitoring).
- Comfort with secure and scalable platform concepts (networking, identity, secrets, infrastructure automation).
- Experience or strong interest in AI\-assisted development as part of daily engineering practice.
LLMs/agent capabilities (expected for this role)
- Hands\-on experience building LLM\-powered tools/agents (prompting, tool use, retrieval where appropriate, and evaluation/quality approaches).
- Ability to design safe and predictable AI systems (validation, fallbacks, human\-in\-the\-loop, and clear failure handling).
Preferred
- Familiarity with enterprise security/compliance expectations (access controls, audit trails, change management, data governance).
- Experience modernizing processes (Lean/ops mindset) and designing systems that align to how teams actually work.
- Experience building internal tools that drive adoption across multiple functions.
Participation in our Recruitment Process: To Be Determined. *Typical interview processes including initial screening w/ a Recruiter, meeting with Hiring Manager (SVP, Strategic Operations \& Corporate Development), and other internal team members the role may closely interact with.*
- Estimated total candidate time investment: Approx. 3hrs
Benefits Why M\-Files?
M\-Files is a global company with Finnish roots, built around a product we are proud of.
You’ll have the opportunity to contribute to our continued growth while developing your own expertise in a collaborative, supportive environment.
Our guiding principles, Make It Happen, Help Others, Love Customers, are reflected in how we work every day, with transparency and strong team spirit at the core of our culture.
What We Offer:
- As remote enabled company our employees enjoy the flexibility to establish their own life/work balance
- Matching 401K Plan (25% of employee's 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 $100K-$150K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($125K) sits 25% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
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: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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