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About This Role
Description:
Job Summary
Springs Window Fashions is a leader in the custom window treatment industry since 1939\. Headquartered in Middleton WI, we have 6,000 associates with locations in North America, Europe, and Asia. Our custom window treatments are available under the Bali, Graber, SunSetter and Mecho brands in nearly every major retailer, in thousands of designer showrooms, and showcased in large commercial buildings.
Our company has made significant investments to become a leader in product innovation. As North America’s premier window covering company, we’re committed to creating a “Best Experience” for our consumers, channel partners and associates. We are bringing new innovations to the market at an accelerated pace and have a variety of offerings to consumers who want to improve their home décor.
This is an engineering role, not a research role. The AI Engineer is a hands\-on builder who designs, ships, and operationalizes AI solutions that go live across the organization and accelerate Springs Window Fashions' enterprise AI strategy. You will write the code, stand up the pipelines, and get real systems into production—partnering directly with Information Technology, business stakeholders, operations, customer service, product development, and analytics teams to deliver AI capabilities that measurably improve efficiency, elevate customer experiences, and sharpen decision making.
The ideal candidate is a software engineer first who happens to be obsessed with AI, pairing strong engineering fundamentals with hands\-on command of machine learning, generative AI, data engineering, automation, and cloud technologies. You move fast and iterate in the open, treating a rough prototype that works as more valuable than a polished plan that doesn't. You thrive in a fast\-paced, transformation\-oriented environment and consistently turn business problems into production\-ready AI solutions rather than pilots that stall in a notebook. Key Responsibilities* Write, test, and ship real AI and machine learning code that makes it into production, with support from senior engineers.
- Build features for generative AI applications—LLMs, RAG, copilots, and automation—that real people across the business actually use.
- Help translate business problems into working prototypes and features, learning the domain and the trade\-offs as you go.
- Build and maintain AI pipelines, APIs, and integrations, then improve them based on real feedback.
- Collaborate with data engineering teams to ensure high\-quality, governed, and accessible data for AI initiatives.
- Develop AI\-enabled analytics and predictive models supporting manufacturing, supply chain, customer service, sales, and operations.
- Follow AI governance, security, and responsible\-AI practices, and help monitor models running in production.
- Help optimize model performance, scalability, reliability, and operational efficiency.
- Explore new AI tools and techniques, and bring fresh ideas and honest assessments back to the team.
- Prototype ideas quickly—failing fast, learning faster, and turning experiments into working demos.
- Create technical documentation, operational procedures, and knowledge transfer materials.
- Write clean, well\-tested code and take part in code reviews to sharpen your craft.
- Pair with senior engineers to learn how enterprise AI systems are designed, shipped, and kept running.
- Grow fast—soak up feedback, ask sharp questions, and share what you learn with the team.
Requirements:
Required Education/Experience* 5\+ years building real software in engineering, machine learning, data engineering, or AI development
- Hands\-on experience shipping machine learning and generative AI solutions into production, not pilots that stalled in a notebook
- Fluent in Python and modern AI/ML frameworks such as TensorFlow, PyTorch, LangChain, or Hugging Face
- Comfortable building on cloud platforms such as Microsoft Azure, AWS, or Google Cloud
- Experience wiring AI solutions into real enterprise systems and APIs
- Solid grasp of AI governance, model lifecycle management, and security best practices
- Sharp analytical and problem\-solving instincts, and the ability to explain your work to an executive in two sentences and to an engineer in two hundred
- Comfortable delivering in fast Agile cycles, iterating in the open rather than waiting for perfect.
Preferred Experience* Experience with Microsoft Copilot, Azure OpenAI, or enterprise generative AI platforms.
- Manufacturing, supply chain, consumer products, or retail industry experience.
- Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.
- Familiarity with data visualization and analytics platforms such as Power BI or Tableau.
- Experience leading enterprise AI transformation initiatives.
Knowledge, Skills \& Abilities* Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
- Advanced degree in Artificial Intelligence, Machine Learning, or Data Science is a plus—but we care far more about what you have shipped than what you have studied.
How We Work to Deliver a Best Experience: Our Culture* Our Core Value: We do the right thing, always
- Highly valued leadership skills include:
+ Empowerment: Encourages innovation and continuous learning. Enables cross\-functional collaboration and technical experimentation.
+ Ownership: Owns solutions end to end: if it breaks, you fix it; if it works, you make it better. Delivers secure, scalable, business\-aligned AI with real urgency and strong execution discipline.
+ Leadership: Influences technical direction and promotes enterprise AI adoption. Communicates effectively with both technical and non\-technical stakeholders.
+ One Springs Team: Collaborates across departments to drive shared business outcomes. Builds strong relationships and trust across the enterprise.
+ Continuous Innovation: Stays ahead of emerging AI trends and tools, separating genuine advances from hype. Relentlessly improves AI capabilities, automation, and operational maturity.
+ Speed: Ships iterative value through rapid build\-and\-deploy cycles, often turning a new technique into a working prototype within days. Balances that speed with the operational stability and governance an enterprise requires.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Springs Window Fashions, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Springs Window Fashions AI Hiring
Springs Window Fashions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Middleton, WI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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