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About This Role
At Trunk Tools, we’re the leading AI company revolutionizing construction, the second\-largest industry on earth. We recently raised a $40M Series B led by Insight Partners, bringing our total funding to $70M from top\-tier investors including Redpoint and Innovation Endeavors. This new round is fueling our next phase of growth as we scale AI agents across the jobsite.
Our mission is to build the future of construction through intelligent automation. Despite being a $13\+ trillion industry, construction still runs largely on analog processes, we’re changing that by embedding AI directly into field operations.
Founded by builders and technologists (Stanford, MIT), our team has delivered software used by over 140,000 field professionals, impacting millions of users and contributing to $10B\+ in built projects. Many of us come from the field ourselves, giving us a deep understanding of the industry’s unique challenges.
After years of building the “brain” of construction, we’re now launching production\-ready AI agents, starting with intelligent document processing and Q\&A, and rapidly expanding into core operational workflows. Our team has doubled in the past year, and with 100\+ employees (50\+ engineers), we’re scaling fast and entering a period of hypergrowth. This is a rare opportunity to join at an inflection point.
Position Overview
Trunk Tools is looking for an experienced Program Manager to own the delivery of our AI\-Enabled GC program inside our largest general contractor accounts. This is the person who runs the entire transformation: the project plan, the meeting cadence, the stage\-gates, and the KPIs that prove it’s working. You’ll be dedicated to top\-20 GCs for multi\-year engagements, co\-owning the account with the Customer Success Manager, who holds the commercial and renewal relationship. You’ll orchestrate a dedicated delivery pod — Forward Deployed Experts and the shared Instructional Design and Learning Operations team — to move an entire organization from AI\-curious to AI\-first. Travel is required for this position, up to 30%.
Key Responsibilities
- Program Ownership \& Execution
+ Own the end\-to\-end program plan across every phase and keep the critical path on track.
+ Run the stage\-gate model: define entry and exit criteria for each phase and hold the program to them.
+ Manage risk, dependencies, and resourcing across the pod, and make the calls that keep the deployment moving.
- Governance \& Cadence
+ Stand up and run the program’s meeting rhythm — executive steering committees, business\-unit working sessions, champion syncs, and office hours.
+ Own executive alignment: keep the customer’s sponsor and leadership engaged, informed, and accountable to their commitments.
+ Lead program business reviews that show progress against KPIs and set the next quarter’s priorities.
- KPIs, Measurement \& Reporting
+ Define and track the adoption and behavioral\-change metrics that prove the program is working — certification rates, active usage, and workflow change by business unit.
+ Correlate broad AI adoption with Trunk Tools outcomes, and report both to the customer and internally.
+ Maintain the executive dashboard and keep a single source of truth for program health.
- Team Orchestration
+ Direct the day\-to\-day work of the delivery pod — the FDX Manager and Forward Deployed Experts, plus the shared Instructional Design and Learning Operations team.
+ Coordinate customer champions and trainers so the organization builds its own capability.
+ Make sure content authoring is resourced and started early — it sits on the critical path before the field can deploy.
- Handoff \& Repeatability
+ Drive the program toward a self\-sustaining state — customer\-owned trainers, governance, and cadence — by the end of the engagement.
+ Feed what you learn back into the shared playbook so the next GC deployment starts faster.
Qualifications
- Experience
+ 6\+ years in program management, engagement management, or technical program management, including large, multi\-stakeholder enterprise programs.
+ Direct experience leading change management or transformation programs — moving people and organizations, not just shipping software.
+ Customer\-facing delivery experience (professional services, consulting, or enterprise CS). Construction, AEC, or enterprise SaaS is a strong plus.
- Skills \& Knowledge
+ Fluent in program\-management practice and tooling (e.g., Asana, Smartsheet); PMP or Agile certification a plus.
+ Grounded in change\-management methodology (e.g., Prosci / ADKAR); able to design cadence, stage\-gates, and adoption metrics from scratch.
+ Strong executive presence — can hold a room of C\-level stakeholders accountable and keep them aligned.
+ A daily AI power\-user who leads by example, plus comfort with CS platforms (e.g., Planhat).
- Education
+ Bachelor’s degree or equivalent experience.
+ Program\-management or change\-management certifications (PMP, Prosci, or similar) are a plus.
Why Trunk Tools?
- Innovative Culture: Work at the forefront of technology with a team that values innovation and continuous learning.
- Impactful Work: Help transform businesses with tools that drive real, measurable outcomes.
- Career Growth: Be part of a growing company with ample opportunities for personal and professional development.
- Competitive Compensation: We offer a competitive salary, benefits, and performance\-based incentives.
- Work\-Life Balance: Enjoy a supportive work environment that encourages a healthy work\-life balance.
What we offer
A close\-knit and collaborative early\-stage startup environment where every voice is heard and every opinion matters
Competitive salary and stock option equity packages
4 Medical Plans to choose from including 100% covered option. Plus Dental and Vision Insurance!
Learning \& Growth stipend
Flexible long\-term work options (remote and hybrid)
Free lunch provided in the office in NYC \& Austin \- you’ll never go hungry with us!
Unlimited PTO; We truly believe in work\-life balance and that hard work should be balanced with time for rest and rejuvenation
IRL / In\-Person retreats throughout the year
*Please note:* *All official communication from Trunk Tools will come from an email address ending in* *@**trunk.tools**. If you receive outreach from any other domain, please disregard it or report it to us.*
*At Trunk Tools, we’re working hard to build a more productive and safer environment within the construction industry, and we strive to live by these same values here at Trunk Tools HQ. As an equal\-opportunity employer, we are committed to building an inclusive environment where you can be you. We work hard to evaluate all employees and job applicants consistently, without regard to race, color, religion, gender, national origin, age, disability, pregnancy, gender expression or identity, sexual orientation, or any other legally protected class.*
Compensation Range: $210K \- $261K
Salary Context
This $210K-$261K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trunk Tools, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($235K) sits 10% above the category median. Disclosed range: $210K to $261K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Trunk Tools AI Hiring
Trunk Tools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $261K - $261K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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