Interested in this AI/ML Engineer role at Monotype?
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About This Role
*Named "One of the Most Innovative Companies in Design'' by Fast Company, Monotype brings brands to life through type and technology that consumers engage with every day. The company's rich legacy includes a library that can be traced back hundreds of years, featuring famed typefaces like Helvetica, Futura, Times New Roman and more. Monotype specializes in the design, development, licensing, and management of typefaces and font technologies for the world’s biggest global brands and individual creative professionals, offering a wide set of solutions that make it easier for them to do what they do best: design beautiful brand experiences.*
*Want to learn more about who we are, what we do, and how you can become part of our team of over 1,000 talented employees across the globe? Visit us at* *www.monotype.com**.*
We are seeking an AI Automation \& Agentic Systems Engineer to help transform enterprise workflows through AI\-first automation and intelligent agent orchestration.
In this role, you will partner closely with teams across the organization — including GTM, Finance, Legal, HR, Support, and Customer Success — to identify high\-impact automation opportunities, redesign manual processes, and implement scalable AI\-driven solutions.
You will build and orchestrate intelligent agent systems using modern LLM platforms, orchestration frameworks, retrieval architectures, and enterprise integrations. This role requires a unique blend of systems engineering, AI architecture, workflow design, and product thinking.
This is a highly cross\-functional, hands\-on engineering role ideal for someone who thrives in ambiguity, enjoys solving operational complexity, and is passionate about applying AI to real\-world business challenges.
*What you’ll be doing:*
- Design and configure AI\-driven workflows and multi\-agent automation systems that streamline and orchestrate business processes
- Build and manage AI automations using low\-code/no\-code orchestration platforms such as n8n, Workato, UiPath, Zapier, Make, AWS Bedrock, Watsonx, and emerging AI agent tools
- Configure AI agents, integrations, and workflows across enterprise platforms and internal business systems
- Translate business and operational requirements into scalable AI\-enabled automation solutions
- Connect systems and tools using APIs, connectors, integrations, and workflow automation technologies
- Support integrations across platforms such as Salesforce, SAP, Slack, Teams, SharePoint, Workday, and other enterprise applications
- Leverage AI platforms including OpenAI, Anthropic, Gemini, Azure OpenAI, and related tooling to support intelligent automation initiatives
- Help implement Retrieval\-Augmented Generation (RAG) workflows and enterprise knowledge retrieval capabilities where applicable
- Establish operational processes, monitoring, and governance practices to ensure reliable and effective AI automations
- Collaborate with business stakeholders, IT teams, and operations leaders to identify automation opportunities and improve workflows
- Contribute to the company’s evolving AI automation and orchestration strategy through continuous improvement and experimentation
*What we’re looking for:*
- Bachelor’s degree in Information Systems, Business Technology, Computer Science, MIS, or a related field preferred
- 3\+ years of experience in IT administration, business systems analysis, automation, workflow configuration, or related technical operations roles
- At least 1 year of hands\-on experience working with AI tools, AI automation platforms, or LLM\-powered workflows
- Experience configuring and orchestrating AI agents or automation workflows using low\-code/no\-code tools rather than traditional software development
- Strong understanding of business process automation and the ability to translate operational needs into scalable workflows
- Familiarity with workflow orchestration, integration, and connector platforms such as MuleSoft, Boomi, Workato, Zapier, UiPath, or similar technologies
- Experience integrating enterprise systems and applications using APIs, connectors, and automation platforms
- Comfortable working across SaaS and enterprise tools such as Salesforce, Slack, Teams, SharePoint, SAP, or Workday
- Strong systems\-thinking and problem\-solving abilities with a focus on automation and operational efficiency
- Ability to work cross\-functionally with business stakeholders and technical teams to implement practical AI\-driven solutions
- Excellent communication skills with the ability to explain technical workflows to non\-technical audiences
- Experience with AI technologies can be emerging/newer experience; practical implementation and curiosity are valued
*Preferred traits:*
- Builder mindset with strong curiosity around AI automation and agentic technologies
- Comfortable navigating ambiguity and evolving business requirements
- Self\-directed with the ability to work independently and take ownership
- Interested in improving operational efficiency through automation and AI\-enabled workflows
- Experiment\-driven mindset with a willingness to continuously learn and adapt to emerging technologies
*What’s in it for you:*
- *Hybrid work arrangements and competitive paid time off programs.*
- *Comprehensive commercial medical insurance coverage to meet all your healthcare needs.*
- *Competitive compensation with corporate bonus program \& uncapped commission for quota\-carrying Sales*
- *A creative, innovative, and global working environment in the creative and software technology industry*
- *Highly engaged Events Committee to keep work enjoyable.*
- *Reward \& Recognition Programs (including President's Club for all functions)*
- *Professional onboarding program, including robust targeted training for Sales function*
- *Development and advancement opportunities (high internal mobility across organization)*
- *Retirement planning options to save for your future, and so much more!*
*Monotype is an Equal Opportunities Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.*
The US pay range for this position is $130,000\.00 \- $155,000\.00 annual base salary for external candidates with the appropriate level of experience. A corporate bonus will also be offered as part of this role. The final annual base salary offered will be based on location and experience level, and could be less for internal applicants depending upon experience. The job application window for this role is 30 days from the posting date.
*\#LI\-DNI*
Salary Context
This $130K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Monotype, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($142K) sits 21% below the category median. Disclosed range: $130K to $155K.
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.
Monotype AI Hiring
Monotype has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Woburn, MA, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 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).
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 3,823 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.
Frequently Asked Questions
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