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About This Role
Description:
Do you find satisfaction in a spotless spreadsheet and a perfectly processed mail file?
At Thysse, precision matters — and so do the people who deliver it. We’re a third\-generation, family\-owned brand experience company that helps clients across retail, healthcare, education, and hospitality make their mark. From large format displays and environmental graphics to direct mail campaigns, we do it all — and we do it right.
We’re looking for a Mail/Data Services Specialist who takes ownership of the details that make direct mail campaigns actually work. You’ll be the expert behind the data — processing mail files, managing variable data, interpreting postal regulations, and making sure every project ships accurately and on time. If you love being the person who catches the error before it becomes a problem, this role was built for you.
What You’ll Do
Manage Mail \& Variable Data
- Process and verify mail files and variable data with a high degree of accuracy
- Deliver mail lists and variable data files to the email team in a timely, organized manner
- Process inbound Business Reply Envelope (BRE) mail pieces through scanning and software applications, preparing output for customers and internal teams
- Perform in\-process and final quality control checks using the mail planning checklist
Own the Data Workflow
- Work efficiently in EFI Pace, Microsoft Excel, and related postal processing software to manage data imports/exports
- Manage complex spreadsheets and generate billing details to support project accuracy
- Update the mailing shipment dashboard to reflect current status and ensure on\-time delivery
- Coordinate with logistics vendors to develop accurate drop ship calculations and cost estimates
Support Clients \& Internal Teams
- Apply knowledge of USPS postal regulations to support accurate project execution and answer day\-to\-day questions from clients and internal teams
- Serve as a secondary point of contact for mail list, postal, and variable data questions, escalating to the Mailing Manager as needed
- Partner with Account Management and Project Management to keep projects moving and clients informed on postal savings opportunities and variable data specifications
Improve \& Automate
- Collaborate with the Mail team and management to develop and refine data workflows
- Coordinate with the Process Improvement and Automation teams to identify repetitive workflows that can be automated
Requirements:
What You Bring
- High school diploma or GED preferred
- 2\+ years of experience with USPS bulk mailing prep regulations in the printing or direct mail industry preferred
- Proficiency in Microsoft Excel required; experience with postal processing software a plus
- Comfort with Microsoft Office Suite and Google Workspace; ability to learn new software quickly
- Strong attention to detail and a commitment to accuracy — you catch errors before they ship
- Ability to manage multiple projects, prioritize effectively, and adapt when priorities shift
Who You Are
You’re not just checking boxes — you’re the one who notices when something doesn’t look right and takes care of it. You work well independently, communicate clearly, and don’t let the details slip through the cracks. You take pride in your work, and you know that in direct mail, the data is everything.
You’re collaborative by nature — comfortable working across teams and keeping clients and colleagues in the loop. And when a process could be smoother, you say something.
Why Thysse
Thysse (tie • see) is a third\-generation, family\-owned commercial printer and brand experience provider located in Oregon, WI, just 15 minutes south of Madison. Our team of 100\+ delivers exceptional service and outcomes for some of the most valuable brands in the nation.
As a brand experience provider, we believe the story a brand tells is as important as the materials it's printed on. From printed campaigns to branded spaces—and everything in between—we get to work on the fun stuff our partners use to promote their brands.
We're always looking for problem\-solvers, creators, and collaborators who care as much about each other as the work they do. We don't shy away from a challenge, we're passionate about delivering exceptional outcomes, and we believe the best partnerships are built on trust. We work hard, have a little fun along the way, and at the end of the day, we're proud to say we did it together.
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 Thysse, 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.
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.
Thysse AI Hiring
Thysse has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oregon, WI, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 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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