Interested in this AI/ML Engineer role at Protolabs?
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About This Role
Be yourself at Protolabs Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. We are an equal opportunity employer, and we are committed to building a diverse team that feels they are valued in the workplace. So do you feel you do not meet every single requirement but still intrigued? We encourage you to still apply! You can help make our company even better. We do not discriminate based on race, color, national origin, sexual orientation, gender, age, mental or physical ability, or any way you represent yourself. We strongly believe diversity makes for more successful teams. Why Protolabs? We are the leaders in digital manufacturing. We hire doers, makers, and creative thinkers who tackle our roles with an entrepreneurial spirit. Our culture is centered around meaningful work that brings new and innovative products to market at unprecedented speeds. We are a diverse team that comes from all walks of life and take pride in our team who is smart, genuine, humble, and passionate about what we do. It’s our people who fuel our creativity and make our culture feel like home.
Join our team as an AI Customer Success Engineer!
This is a HYBRID role - onsite Tuesday-Thursday in Maple Plain, MN.
Companies are investing heavily in AI yet employees often use only a fraction of what those tools and solutions can do, and the friction remains. The capability exists. The problem exists. What's missing is the connection between them. Do you enjoy solving problems with technology to realize true business value? You can be the bridge between technology and business!
As an AI Customer Success Engineer, you will embed yourself with business users to deeply understand their workflows and use cases, build automations and agents through no-code development, analyse user feedback, and drive adoption of AI tooling turning untapped potential into real impact. Beyond enablement, you will create the analytical depth behind custom AI solutions by analysing requirements, conducting demos, guiding users through rollout, and managing feedback channels, working alongside Technical Product Managers to deliver measurable value through AI across the organisation.
What You Will Do:
AI Platform Adoption & Enablement; No-Code Development (Primary)
- You will sit with business users to deeply understand their workflows and use cases for AI tooling
- You will help create and configure agents, apps and automations within AI tooling for and with end-users
- You will process and analyse user feedback; surface gaps between user expectations and current capabilities
- You will develop onboarding materials and guide users through adoption
- You will derive requirements for platform set-up based on user needs
- You will oversee support channels, collect and answer user feedback and questions
- You’ll turn real business challenges into effective no-code and low-code solutions, taking ideas from concept through to implementation
Use Case Analysis & Requirements Support (Secondary)
- You will support requirements analysis for custom AI use cases: elicit, document, and refine functional and non-functional requirements with business stakeholders
- You will perform use case scoping and refinement — translate business needs into structured AI solution requirements
- You will define measurable KPIs and acceptance criteria for use case delivery
- You will conduct demos and feedback calls with business users
What You Bring:
- Bachelor's degree in a relevant field (e.g., Computer Science, Information Systems, Business Informatics, Industrial Engineering, or Digital Business)
- 3–5 years of professional experience in customer success engineering, AI/digital solutions consulting, business analysis, or technical product enablement
- Extensive hands-on experience building solutions with no-code/low-code AI platforms — creating agents, automations, workflows, and apps using LLM-based assistants, agent frameworks, and prompt engineering. Able to independently deliver AI-powered solutions without custom development.
- Demonstrated ability to work across diverse business domains (e.g., manufacturing, engineering services, finance, HR, supply chain) and quickly understand domain-specific processes, terminology, and pain points.
- Strong understanding of how enterprise systems connect — APIs, integrations, data flows, and SaaS architectures. Able to assess how AI tools fit into existing system landscapes and identify integration requirements.
- Excellent verbal and written communication skills; able to present complex technical concepts to non-technical audiences, facilitate workshops, run demos, and build trusted relationships with business stakeholders at all levels.
- Proven ability to elicit, document, analyse, and validate business and technical requirements (e.g., user stories, acceptance criteria, process flows) through stakeholder interviews and hands-on observation.
- Natural ability to sit with end-users, understand their real-world challenges, and translate messy problems into structured use cases and actionable solutions.
- Ability to evaluate AI use cases for feasibility, business value, and priority — and identify when a use case requires custom development versus no-code implementation.
- Experience driving enterprise tool rollouts, user adoption programs, and change management — including onboarding, training, feedback loops, and continuous improvement.
- Ability to produce clear requirement documents, process maps, user guides, and portfolio overviews.
What Sets You Apart:
- Agile Familiarity: Comfortable working in sprint-based delivery, writing PBIs/features, and collaborating closely with development teams
- Background in manufacturing, engineering services, or B2B technology companies
- Familiarity with AWS services and AI governance frameworks
- Scrum, or Product Owner certification
What's In It For You:
- We offer a competitive salary and total rewards package
- We offer amazing benefits including but not limited to: + Health Insurance: Traditional OR High Deductible plan + Flexible Spending Accounts + Health Savings Account (including employer contributions) + Dental and Vision + Basic and Supplemental Life Insurance + Short-Term and Long-Term Disability + Paid caregiver leave
- You will receive at least 3 weeks of PTO + Wellness + Holiday Pay + Volunteer Hours
- 401k with company match and immediate vest
- Employee Stock Purchase Program at a discounted rate
- Matching grants through Protolabs foundation
- And More!
Protolabs offers a competitive total rewards package, and compensation mix vary based on position, such as base wage, as well as other earnings including incentives and commissions. Actual pay offered will vary depending on multiple factors which may include, without limitation, job function, work experience, specialized skills, training, education, and certifications.Proto Labs maintains ITAR-compliant operations in all of our United States based facilities. Due to ITAR regulations, this role is only open to U.S. Citizens, lawful permanent residents (green card holders) or foreign nationals granted refugee or asylee status. Individuals with temporary visas (e.g. E, F-1, H-1, H-2, L, B, J, TN or OPT) are not eligible for hire in this role. Proto Labs, Inc. is an Equal Opportunity EmployerPhysical Demands:While performing the essential duties of this job, the employee is regularly required to sit; use a computer keyboard, monitor and mouse, telephone and printer; reach with hands, and arms, talk, see and hear. The employee is occasionally required to stand, walk, stoop or kneel and must occasionally lift and/or move up to 25 pounds.Work Environment:Indoors (A/C); nonsmoking; the majority of this job function is performed in an office setting requiring normal safety precautions. However, there may be job duties that require the employee to spend time in the manufacturing areas exposed to machinery and noise; eye protection occasionally required. Occasionally works in outside weather conditions. Occasionally works near moving mechanical parts and in high, precarious places and is occasionally exposed to wet and/or humid conditions, fumes or airborne particles, toxic or caustic chemicals, risk of electrical shock and vibration. The noise level in the work environment is usually quiet to moderate.
Salary Context
This $107K-$161K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Protolabs, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($134K) sits 37% below the category median. Disclosed range: $107K to $161K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Protolabs AI Hiring
Protolabs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Maple Plain, MN, US. Compensation range: $161K - $161K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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