Interested in this AI/ML Engineer role at STACK Construction Technologies?
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About This Role
About Us:
STACK is a leading provider of cloud\-based construction estimating and takeoff software solutions, committed to helping businesses transform through innovative solutions. We pride ourselves on fostering a collaborative, dynamic environment where team members have the opportunity to grow and make a real impact.
About the Role:
The *Principal ML / Computer Vision Engineer* is a senior technical leader responsible for designing, developing, and deploying advanced machine learning and computer vision solutions that power core product capabilities. This role drives innovation across model development, data pipelines, and production systems, ensuring scalable, high\-performance solutions aligned with business objectives.
The role partners closely with Product, Engineering, and Data teams to translate complex business problems into robust ML/CV systems, while setting technical direction, mentoring engineers, and establishing best practices across the ML lifecycle.
Your Most Important Initiatives:
- Lead the design and development of machine learning and computer vision models for production use
- Define and drive ML/CV technical strategy and architecture
- Build and optimize data pipelines for training, evaluation, and deployment
- Collaborate cross\-functionally to translate business needs into ML solutions
- Deploy and monitor models in production, ensuring performance and scalability
- Establish best practices for experimentation, versioning, and reproducibility
- Improve model accuracy, latency, and robustness through continuous iteration
- Lead code reviews, mentor engineers, and elevate team technical standards
- Evaluate new tools, frameworks, and approaches in ML and computer vision
- Ensure data quality, labeling strategies, and model validation processes
- Contribute to roadmap planning and technical decision\-making
- Drive innovation in deep learning, image recognition, and video analysis
What You Bring:
- 8\+ years of experience in machine learning, computer vision, or related fields
- Strong expertise in deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience building and deploying production ML systems
- Proficiency in Python and ML tooling ecosystem
- Strong background in computer vision techniques (e.g., object detection, segmentation, OCR)
- Experience with cloud platforms (AWS, GCP, or Azure)
- Knowledge of data engineering and large\-scale data processing
- Proven ability to lead technical projects and mentor engineers
- Strong problem\-solving and analytical skills
- Excellent communication and collaboration abilities
- Advanced degree (MS or PhD) in Computer Science, AI, or related field preferred
Why Join STACK?
- Opportunity to work in a fast\-paced, growth\-oriented, remote\-first environment.
- Gain exposure to multiple aspects of the company including Marketing, Sales, Customer Support, and Training.
- Be part of a dynamic, supportive team where your contributions are valued.
At STACK, our values shape how we work, collaborate, and serve our customers:
- Radical Honesty: Communicate directly, respectfully, and transparently—even when conversations are difficult. Give and receive feedback with care.
- Customer Obsession: Put the customer at the center of every decision. Solve real problems, follow through, and measure success by customer outcomes.
- Move With Purpose: Take thoughtful action, remain accountable, and own both successes and mistakes. Anticipate what’s next and adapt as you learn.
- Grow Together: Share knowledge, collaborate across teams, and invest in one another’s development. Celebrate collective wins and treat setbacks as opportunities to learn.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be.
We are interested in every qualified candidate who is eligible to work in the United States. However, we are not able to sponsor visas.
We take into account an individual’s qualifications, skillset, and experience in determining final salary. This role is eligible for health insurance (medical, dental \& vision), life insurance, 401(k) \& paid time off. The expected compensation range for this position is about $190k\- $210k USD. The actual offer will be at the company’s sole discretion and determined by relevant business considerations, including the final candidate’s qualifications, years of experience, skillset, and geographic location. \#LI\-Remote
Salary Context
This $190K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At STACK Construction Technologies, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($200K) sits 9% below the category median. Disclosed range: $190K to $210K.
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.
STACK Construction Technologies AI Hiring
STACK Construction Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US. Compensation range: $210K - $210K.
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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