Interested in this AI/ML Engineer role at Mark III Construction?
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This is NOT a REMOTE OR HYBRID position
The Director of AI is a strategic leadership role responsible for identifying, evaluating, and driving the adoption of artificial intelligence and automation technologies across Mark III Construction's business functions — including MEP Construction, Service, Manufacturing, Virtual Construction (VDC), Business Development, Estimating, and Finance.
This role chairs the company's AI Committee, sets AI strategy and governance, and partners with departmental leaders to identify high-value use cases, pilot solutions, and scale what works. This is a strategy and transformation role, distinct from IT infrastructure and support, though close coordination with IT is required for security, data access, and technical implementation.
This position will be responsible for the AI responsibilities for all operations. We also will be hiring a Vice President of Technology for M3 Components, LLC. The AI Director will have authority for the full enterprise, but the Vice President of Technology will responsible solely for the technology of M3 Components, LLC.
Who we are:
Mark III is a full-service construction company comprised of three complementary business units: M3 MEP, M3 Service, and M3 Components. We support projects from initial concept through long-term maintenance, with specialized expertise in healthcare, higher education, industrial, life science, and technology markets.
Our integrated, big-picture approach allows us to self-perform electrical, HVAC, plumbing, process piping, prefabrication, and MEP design. By consolidating these services under one organization, clients benefit from reduced profit layers, minimized scope gaps, and simplified project schedules—working with a single, accountable partner rather than multiple consultants and subcontractors.
Headquartered in Sacramento, California, Mark III operates throughout California and Nevada. Our state-of-the-art manufacturing facility serves as a centralized hub supporting five core trades: HVAC, mechanical piping, plumbing, electrical, and metal-stud framing. To support our continued growth, we will be relocating from our current combined 62,000 square feet of office and operational space to a new 340,000-square-foot headquarters at 2031 Optisolar Lane, with the move planned for completion by the end of Q3 2026.
Mission:
Leading the Evolution of Construction
Vision: Exposing the World to a New Way to Build
Core Values:
- Teamwork
- Excellence
- Perseverance
- Integrity
- Continuous Improvement
Key Responsibilities
AI Strategy & Governance
- Develop and maintain a company-wide AI strategy and roadmap aligned to business goals
- Chair the AI Committee, with cross-functional representation from MEP, Service, Manufacturing, VDC, Business Development, Estimating, and Finance
- Enforce AI governance policies: acceptable use, data privacy, vendor risk, and model/tool approval process
- Own the build-vs-buy-vs-partner decision framework for AI tools and vendors
Cross-Functional Use Case Development
- Work directly with department leaders to identify pain points and high-ROI AI/automation opportunities (e.g., estimating takeoffs, VDC clash detection, prefab optimization, service dispatch, AR/collections forecasting, proposal generation)
- Prioritize use cases based on impact, feasibility, and cost
- Run structured pilots with clear success and failure criteria before scaling
Implementation & Change Management
- Lead deployment of approved AI tools and workflows in partnership with IT (technical execution) and department champions (adoption)
- Build training programs and internal documentation to drive adoption and reduce shadow-IT / unsanctioned tool use
- Manage change resistance — often the hardest part of AI rollouts in a field-heavy, non-union trades organization
Vendor & Technology Evaluation
- Evaluate emerging AI vendors and tools relevant to construction, MEP, manufacturing, and finance
- Manage AI-related vendor contracts, licensing, and budget in coordination with Finance and Procurement
- Maintain awareness of competitive and industry AI adoption trends in construction technology
Risk, Compliance & Data
- Partner with the IT Support Manager on data security, access controls, and infrastructure needs for AI tools
- Ensure AI tool usage complies with client contracts, data confidentiality obligations, and regulatory considerations (e.g., data center and medical client sensitivity)
- Monitor and mitigate risks around AI hallucination or errors in customer-facing or safety-related outputs
Executive Reporting
- Report AI initiative status, ROI, and roadmap to the CFO and executive leadership on a regular cadence
- Translate technical AI capability into business-language value propositions for non-technical stakeholders
- Document amount saved or cost saved from AI strategies implemented and will develop a scorecard indicating how we are winning by utilizing AI strategies.
Requirements Education & Experience
- Bachelor’s degree in Computer Science, Information Technology, Business, Engineering, Data Science, or a related field; equivalent combination of education and experience may be considered.
- 7+ years of progressive experience in AI, automation, technology, digital transformation, data analytics, or a related field.
- Demonstrated experience leading enterprise-wide AI, automation, or technology transformation initiatives.
- Experience identifying business problems and translating them into practical AI or automation solutions.
- Experience working across multiple business functions and influencing leaders without direct authority.
- Experience evaluating, selecting, implementing, and managing technology vendors and AI solutions.
- Experience developing business cases and measuring ROI, cost savings, productivity improvements, and other business outcomes.
AI & Technology Knowledge
- Strong understanding of current AI technologies, including generative AI, machine learning, automation, AI-enabled business applications, and emerging AI tools.
- Ability to evaluate AI technologies based on business value, feasibility, security, cost, risk, and scalability.
- Understanding of AI governance, data privacy, information security, vendor risk, and responsible AI practices.
- Ability to understand technical concepts and translate them into practical business applications for non-technical leaders.
- Familiarity with AI applications within construction, MEP, manufacturing, VDC, estimating, service, business development, or finance is highly desirable.
Leadership & Business Skills
- Demonstrated ability to develop and execute a company-wide AI strategy and roadmap.
- Strong project and change-management skills, including the ability to drive adoption among employees with varying levels of technical proficiency.
- Ability to lead cross-functional committees, working groups, pilots, and implementation teams.
- Strong communication and presentation skills, with the ability to communicate AI strategy and value to executive leadership.
- Strong analytical and problem-solving skills.
- Ability to prioritize initiatives based on business impact, feasibility, cost, and risk.
- Demonstrated ability to manage competing priorities and move initiatives from concept through implementation and measurable results.
- Strong vendor negotiation, contract, licensing, and budget-management skills.
Physical Requirements The physical demands described below are representative of those that must be met by an employee to successfully perform the essential functions of this position, with or without reasonable accommodation.
- Ability to sit and work at a computer workstation for extended periods of time.
- Ability to regularly use a computer, keyboard, mouse, telephone, and other standard office technology.
- Ability to communicate effectively in person, by telephone, and through virtual meeting platforms.
- Ability to occasionally stand, walk, bend, reach, and move throughout office, manufacturing, and project environments.
- Ability to occasionally travel to company offices, manufacturing facilities, project sites, and other business locations.
- Ability to safely navigate typical office, manufacturing, and construction environments, including uneven surfaces and active work areas when required.
- Ability to occasionally lift and carry materials, equipment, or supplies weighing up to 25 pounds.
- Ability to maintain regular and punctual attendance and work the hours necessary to perform the essential functions of the position.
- Ability to perform the essential functions of the position with or without reasonable accommodation.
Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Training & Development
- Onsite Gym and kitchen
Salary Range $160,000 - $180,000 DOE also eligible to participate in Mark III Annual Bonus Plan
"Mark III Construction and its affiliated companies is an equal opportunity employer regardless of race, color, caste, sex, gender, age, sexual orientation, gender identity, gender expression, religion, marital status, genetic information, national origin, ancestry, citizenship, physical or mental disability, medical condition, protected veteran or military status, domestic violence victim status, political affiliation, or any other basis protected by federal, state, or local law."
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Mark III Construction, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Mark III Construction AI Hiring
Mark III Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sacramento, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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