Interested in this AI/ML Engineer role at ButterflyMX?
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Our Mission
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*ButterflyMX is on a mission to empower people to automate property access, operations, and security from a single platform. Our products are installed in more than 20,000\+ multifamily, commercial, gated communities, and student\-housing properties worldwide, including properties developed, owned, and managed by the most trusted names in real estate. Our features are designed for developers, owners, property managers, and tenants and our products lower operating costs and improve tenant satisfaction.*
Our Solution
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*Developers and owners no longer need to run building wiring or install in\-unit hardware. Property managers can grant building access, revoke permissions, and review entry logs from an online dashboard. Residents can open doors from their smartphones, issue visitor access, and see who is trying to enter the building.*
Our Culture \& Values
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*Fantastic people are the key to our success. As a distributed, primarily remote workforce, we’re looking for more intelligent, passionate, collaborative, ai\-forward, and down\-to\-earth individuals to join our growing team. We’re driven by a shared commitment to excellence and innovation, grounded in our core values: We delight our customers, We take ownership, We are a community of collaborators, We speak up, We think big and do small, and We are tenacious.*
*ButterflyMX is looking for a Senior Full Stack Engineer to join our stellar team that focuses on building world class products for the built world. Our mission is to revolutionize how people access the buildings that they live and work in.*
Role Overview
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We are seeking to hire a Director of AI Strategy \& Transformation to lead our company’s evolution into an AI\-enabled organization. The role is responsible for defining, executing, and continuously refining our AI strategy to ensure it delivers meaningful business value. This role owns AI budgeting, establishes governance frameworks, and drives adoption across the organization.
This is a hybrid strategic and hands\-on role: part visionary, part operator, part evangelist. You will be advocating for AI solutions, coordinating cross\-functional initiatives, and building the operational infrastructure that enables teams to leverage AI at scale.
You will not be expected to build models or write production code, but you must deeply understand what modern AI can and cannot do, how it fits into real workflows, and how to enable teams to capture value safely and efficiently.
Responsibilities
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- Define and own the company’s AI transformation strategy, ensuring alignment with business goals, operating plans, and long\-term vision.
- Develop and maintain an AI transformation roadmap, balancing near\-term wins with longer\-term strategic investments.
- Partner with executive leadership to translate business priorities into AI\-enabled opportunities.
- Manage headcount planning and hiring strategy for AI teams
- Establish processes for AI project intake, prioritization, and resource allocation
- Identify, prioritize, and size high\-impact AI use cases across the business.
- Build and manage AI\-related budgets, including tooling, vendors, experimentation, and enablement initiatives.
- Ensure decisions are driven by data with clear KPIs
- Act as a visible AI champion across the organization.
- Translate complex AI concepts into clear, practical language for non\-technical audiences.
- Foster excitement while maintaining credibility and realism.
- Continuously experiment with emerging AI tools, platforms, and workflows.
- Identify AI tools and services that should be adopted by our company
- Share learnings quickly and pragmatically with teams.
- Ensure AI adoption is secure, compliant, and responsible by establishing governing policies
- Balance risk management with speed and usability.
- Help define guardrails that enable innovation.
Requirements
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- Proven track record leading cross\-functional initiatives and driving organizational change
- Deep understanding of AI technologies, capabilities, and limitations across the current landscape
- Experience managing organizational budgets
- Strong stakeholder management skills with ability to influence at executive level
- Experience establishing governance frameworks and policies
- Demonstrated ability to translate technical concepts for non\-technical audiences and vice versa
- Deep curiosity and a hands\-on mindset with emerging technology
- Would be preferred to have background strategy, operations, technology leadership or consulting
Compensation
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The expected base salary range for this position is $200,000 \- $300,000\. Actual compensation will depend on factors including budget, skills, experience, location, and internal equity. This position may also be eligible for bonuses, equity, or other forms of compensation, where applicable.
Benefits
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- Comprehensive Medical, Dental and Vision plans (ButterflyMX covers 80% of the cost) starting day 1
- 401(k) plan with a match
- 10 paid holidays, 20 vacation days, 5 sick days, 3 floating holidays
- Basic Life and Accidental Death and Dismemberment Insurance (ButterflyMX covers 100% of the cost)
- Short and Long Term Disability (ButterflyMX covers 100% of the cost)
- Paid Family Leave
- Employee Assistance Program
- Quarterly self\-care stipends
- Access to optional benefits including pre\-tax flexible healthcare spending accounts (FSA and HSA), Dependent Care FSA, and Commuter Benefits, as well as optional Supplemental Life, AD\&D, Hospital Indemnity, Legal, Accident, Critical Illness, Pet, and Personal Liability Insurance
- And more!
*ButterflyMX is an equal opportunity employer and we 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. You must have the authorization to work in the US to become an employee. We strive to create an accessible and inclusive experience for all candidates and employees. If you need reasonable accommodations during the application or the recruiting process, please let our recruiting team know.*
Salary Context
This $200K-$300K 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 ButterflyMX, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($250K) sits 14% above the category median. Disclosed range: $200K to $300K.
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.
ButterflyMX AI Hiring
ButterflyMX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $300K - $300K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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