Interested in this AI/ML Engineer role at PTC?
Apply Now →Skills & Technologies
About This Role
Our world is transforming, and PTC is leading the way. Our software brings the physical and digital worlds together, enabling companies to improve operations, create better products, and empower people in all aspects of their business.
Our people make all the difference in our success. Today, we are a global team of nearly 7,000 and our main objective is to create opportunities for our team members to explore, learn, and grow – all while seeing their ideas come to life and celebrating the differences that make us who we are and the work we do possible.
AI Operations Lead (AI Engineer, Marketing OS)
About PTC
PTC (NASDAQ: PTC) is a global industrial software company that enables manufacturers to transform how they design, make, operate, and service products. Our solutions are used by some of the world's leading companies across aerospace, automotive, electronics, medical devices, and industrial manufacturing.
As a leader in digital transformation, PTC is investing in next-generation technologies, including artificial intelligence, automation, and cloud-native platforms, to drive innovation internally and for our customers.
About the Team
You'll join the AI Infrastructure team within Growth Marketing, working across industry and vertical marketing, demand generation, product marketing, customer marketing, brand and communications, and marketing operations. Our mandate is to rebuild how marketing work gets done — moving from documents and manual handoffs to data, AI agents, and automated workflows. As one of the first hires on this team, you will help define and scale AI-powered marketing operations across the organization.
Position Summary
PTC is seeking an Agentic Operator to design, build, deploy, and scale the AI-powered systems that run GrowthOS. You will partner with marketers, business stakeholders, and technical teams to identify automation opportunities, build intelligent workflows, and enable teams to operate more efficiently through AI-enabled tools.
You will work directly with end users — embedding with marketing teams to understand their workflows, translate business challenges into technical solutions, and build systems that automate repetitive work so marketers can focus on strategy, judgment, and customer engagement.
Key Responsibilities
AI Agent Development & Architecture
- Design and build AI-powered workflows and agent-based systems supporting marketing operations
- Build reusable agent architectures — skills, tools, memory, orchestration, evaluation frameworks, and guardrails
- Maintain prompt libraries, workflow templates, and automation frameworks
- Evaluate emerging AI technologies and recommend adoption opportunities
Workflow Automation & Orchestration
- Build and maintain workflow automation using orchestration platforms such as n8n, Zapier, Make or Claude Code
- Design end-to-end processes connecting data, AI agents, and business systems
- Develop automation frameworks that scale across marketing functions
Enterprise Integrations
- Integrate AI-powered solutions with Salesforce, Eloqua, SiteCore, and internal data platforms
- Build MCPs, APIs, connectors, and automation frameworks supporting data-driven decisions
- Ensure data quality, reliability, security, and scalability
AI Quality, Governance & Observability
- Establish monitoring, logging, evaluation, and reporting for AI-powered systems
- Build validation frameworks and quality controls for reliable outputs
- Build governance mechanisms supporting compliance, brand integrity, and auditability
- Identify system failures, regressions, and improvement opportunities
Change Management & Enablement
- Partner with marketing teams to understand business objectives and challenges
- Train and coach users on AI-enabled workflows and tools
- Drive adoption and long-term utilization of AI solutions
- Document processes, playbooks, and best practices to support scalability
What to Send Us
Resumes are welcome, but the strongest signal is evidence. With your application, please include any of:
- Links to agents, tools, or workflows you've shipped (production, not demos)
- A short write-up of a system you built that others came to depend on — what you built, what broke, what you learned
- A GitHub repo, a Loom, or a live link we can review
- The shortest possible answer to: what's the most boring marketing workflow you could automate end-to-end this week?
Qualifications
Required
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or related field, or equivalent practical experience
- 5+ years of software engineering experience
- 2+ years hands-on experience building and deploying production AI or LLM-powered applications
- Strong Python development experience
- Experience with LLM APIs (Anthropic Claude, OpenAI GPT, Gemini, etc…)
- Experience with workflow automation/orchestration platforms (n8n, Airflow, Temporal, Zapier, or comparable)
- Experience integrating enterprise applications, including CRM, marketing automation, and business systems
- Strong understanding of prompt design, evaluation methodologies, and production AI operations
- Excellent communication and stakeholder management skills
Preferred
- Experience designing and deploying multi-agent systems
- Knowledge of agent orchestration frameworks, MCP, and tool-use architectures
- Experience implementing retrieval-augmented generation (RAG)
- Familiarity with MLOps, evaluation frameworks, and AI observability platforms
- Experience building internal platforms or tools with measurable organizational adoption
- Experience in marketing technology, marketing operations, or demand generation
- Experience implementing AI governance, validation, and audit-trail frameworks
What Success Looks Like
- Agents live and running for priority verticals, with speed and quality gains measurable against baseline
- Net-new campaign delivery time reduced from months to weeks
- Marketing teams successfully adopting and expanding AI-driven workflows on their own
- Repeatable frameworks established for AI automation, governance, and performance measurement
What Success Looks Like
Within this role, you will:
- Deliver AI-powered solutions that measurably improve marketing productivity and operational efficiency
- Enable marketing teams to successfully adopt and expand AI-driven workflows
- Build scalable systems that can be leveraged across multiple marketing functions and regions
- Establish repeatable frameworks for AI automation, governance, and performance measurement
- Help define how enterprise AI capabilities are deployed and adopted across the marketing organization
Why Join PTC
- Opportunity to help shape AI transformation within a global technology company
- Work directly on highly visible initiatives with executive sponsorship
- Influence how marketing operations evolve through AI and automation
- Partner with business and technical leaders across the organization
- Build innovative solutions with real-world impact at enterprise scale
Travel Requirements
- Boston-area candidates preferred
- Remote U.S. candidates will be considered
- Limited travel may be required for team collaboration, workshops, and business meetings
PTC is an Equal Opportunity Employer
PTC is proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, or any other characteristic protected by applicable law.
This position is not eligible for employment-based immigration sponsorship. Applicants must be authorized to work in the United States without requiring current or future sponsorship from PTC.
PTC carefully considers a wide variety of factors when determining compensation. The anticipated annual salary range for this position is between $165,000 - 253,000. The salary range reflects a good-faith estimate of compensation at the time of posting. Actual compensation may vary based on a candidate's skills, qualifications, experience, and location. Additionally, candidates may be eligible for a performance-based bonus. Employees also have the opportunity to become a PTC shareholder through our employee share purchase program (ESPP), which allows for the purchase of discounted PTC stock. Certain roles may also be eligible for participation in our equity programs. Employees may be eligible for medical, dental and vision insurance, paid time off and sick leave, tuition reimbursement, 401(k) contributions and employer match, flexible spending accounts, life insurance, disability coverage and, if you are an office-assigned employee, a generous commuter subsidy. All total rewards and benefits programs are
subject to applicable plan eligibility and other terms and conditions.
For more information about PTC’s comprehensive benefits, please visit our Careers Page.
Applications will be accepted on an on-going basis.
This position is not eligible for employment-based immigration sponsorship. Applicants must be authorized to work in the United States without requiring current or future sponsorship from PTC.
At PTC, we believe in the power of diverse ideas and perspectives. As a global company that values and respects all identities, cultures, and perspectives, we strive to create an inclusive PTC for ALL through an environment where everyone feels like they belong and are empowered to bring their true, authentic selves to work. Proud to be an Equal Opportunity Employer, we welcome applicants from all backgrounds and hire without regard to race, national origin, religion, age, color, ethnicity, ancestry, marital status, sex (including pregnancy), sexual orientation, gender identity, gender expression, genetic information, disability, veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
For more information about PTC’s comprehensive benefits and our AI usage, please visit our Careers Page (https://www.ptc.com/en/careers/united-states-careers). Applications will be accepted on an on-going basis until the opportunity is filled.
PTC endeavors to make ptc.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact PTC's Talent Acquisition team at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Life at PTC is about more than working with today’s most cutting-edge technologies to transform the physical world. It’s about showing up as you are and working alongside some of today’s most talented industry leaders to transform the world around you.
If you share our passion for problem-solving through innovation, you’ll likely become just as passionate about the PTC experience as we are. Are you ready to explore your next career move with us?
We respect the privacy rights of individuals and are committed to handling Personal Information responsibly and in accordance with all applicable privacy and data protection laws. Review our Privacy Policy here ."
Salary Context
This $165K-$253K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PTC, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $165K to $253K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
PTC AI Hiring
PTC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $253K - $253K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.