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About This Role
Compensation: $200,000\-$250,000 per year
Location: United States – Remote or Omaha, NE \- Hybrid
The job:
As a Forward Deployed AI Engineer at Buildertrend, you are the founding engineer on our Central AI Enablement team. You’ll define how AI is architected, deployed, and scaled across Buildertrend. You’ll build the foundational AI platform and shared capabilities that enable Sales, Marketing, Customer Success, Finance and People teams to operate more effectively. Unlike a traditional product engineering role, you'll focus on building Buildertrend's internal AI platform and capabilities, partnering directly with internal stakeholders and business leaders to solve high\-impact operational challenges rather than shipping customer\-facing product features. You'll have broad ownership of technical direction, establishing engineering standards and making foundational architectural decisions that shape how AI is built across Buildertrend.
What you will do:
- Own the technical architecture for Buildertrend’s internal AI platform, designing reusable services, agentic systems, workflow orchestration, and AI capabilities that power teams across the GTM and G\&A functions.
- Establish the engineering standards for internal AI development, including architecture patterns, reusable components, evaluation frameworks, observability, governance, and deployment practices.
- Architect the integration and data layer that connects Buildertrend’s business tools, including Salesforce, Gong, Databricks, and others, into a scalable internal AI platform.
- Lead technical evaluations and build\-vs\-buy decisions for AI platforms, frameworks, and tooling in partnership with the Director of AI Enablement.
- Establish best practices for monitoring, evaluating, and continuously improving AI systems in production to ensure reliability, security, and business value.
- Translate ambiguous business problems from non\-technical partners into well\-scoped technical solutions and drive them to completion independently.
- Serve as the technical voice of the Central AI Enablement function in cross\-functional conversations, including engineering leadership and business VPs.
- Embrace using AI as a tool to enhance your work, while remaining open to learning, applying critical thinking, and owning the quality of your final work.
Who you are and what you need:
- 5\+ years of software engineering experience, including at least 2 years of experience designing and deploying production AI applications using modern LLM APIs and orchestration frameworks.
- Demonstrated experience owning technical architecture and influencing engineering direction across multiple teams.
- Experience with modern AI tooling such as OpenAI, Anthropic, LangGraph, MCP, vector databases, or similar technologies.
- Prior experience at a B2B SaaS company is highly preferred.
- Strong Python and/or JavaScript skills, with experience with API integrations, webhook design, and data pipeline architecture.
- Experience designing and delivering multi\-step AI systems.
- Comfortable working across the GTM tech stack, including Salesforce, Gong, or similar, and ability to ramp quickly on new tools and become the technical owner for those systems and integrations.
- Understanding of how sales and marketing funnels work, what revenue teams care about, and how to size and measure the impact of what you build.
- Strong communicator who can present technical tradeoffs, architectural decisions, and build\-vs\-buy recommendations to VPs and senior leadership.
- You’re energized by ambiguous problems where there isn’t an established playbook!
We are giving you:
- Exceptional health packages, including medical, dental, and vision coverage, plus life insurance and short\- and long\-term disability benefits.
- A 401(k) plan with Buildertrend matching contributions to help you plan for the future.
- Generous paid time off, 11 paid holidays, plus personal days to make sure you have time to recharge.
- Parental leave and paid sabbaticals to support you during life’s big moments.
- Volunteer time off – because giving back matters.
- Wellness program and onsite fitness center to keep you feeling your best.
- Opportunities for hybrid or remote work to give you the flexibility you need.
- Technology reimbursement to help cover costs for the tech you need to do your job from home.
- Free daily lunches when you're at our HQ office, plus monthly events to connect with your team.
Who we are:
Great builders don’t just manage projects – they run successful businesses. That’s where Buildertrend comes in. As the leading residential construction management platform, we give contractors the power to control their financials, schedules, team workflows and client relationships – all in one system. No more juggling disconnected tools or guessing on profitability. With nearly two decades of industry expertise, Buildertrend helps builders work smarter, scale faster and stay ahead of the competition. If you want to learn more about us, check out: https://buildertrend.com/about/
Working at Buildertrend:
At Buildertrend, we fully recognize that we all work so we can live better lives—we appreciate and respect that this is a job and not your whole life. What makes Buildertrend so special is a commitment to ensuring you can have the best job, work with the best people, and live your best life outside of work. Our goal is to create a culture where everyone can make an impact on our customers, communities, and each other. In short: We want you to be who you are, love what you do, and build your best life.
*Buildertrend Solutions, Inc. is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee based on race, including natural or protective hairstyle, color, religion, creed, national origin or ancestry, ethnicity, sex (including gender, pregnancy and pregnancy\-related conditions, childbirth, breastfeeding, sexual orientation, gender identity, gender expression, sexual orientation, reproductive decision\-making), age, physical or mental disability, veteran or military status, genetic information, citizenship, marital status, or any other legally recognized protected basis under federal, state, or local law. The information collected by this application is solely to determine suitability for employment, verify identity, and maintain employment statistics on applicants.*
*Applicants with disabilities may be entitled to reasonable accommodation under the Americans with Disabilities Act and certain state or local laws. A “reasonable accommodation” is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on Buildertrend Solutions, Inc. Please inform the company's personnel representative if you need assistance completing this application or to otherwise participate in the application process. To see the complete list of Essential Job Functions, visit* *https://buildertrend.com/essential\-job\-functions\-notice/*
Salary Context
This $200K-$250K range is above the 75th percentile 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 Buildertrend, 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. Disclosed range: $200K to $250K.
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.
Buildertrend AI Hiring
Buildertrend has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US. Compensation range: $250K - $250K.
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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