Interested in this AI/ML Engineer role at McKinstry?
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Build the future, spark innovation and align your career with purpose.
McKinstry is innovating the waste and climate harm out of the built environment and creating lasting impact. Together, we’re building a thriving planet.
Buildings are a leading contributor to the climate crisis, generating nearly 40% of total global energy\-related carbon emissions. We’re making a lasting impact on our industry and within our communities by addressing the climate, affordability and equity crises through:
- renewables and energy services
- engineering and design
- construction and facility services
To get where we’re going, we need big thinkers, problem solvers and collaborative mindsets. Does that sound like you?
The Opportunity with McKinstry
Our team is seeking a Director, AI Strategy \& Operations – Corporate Services Unit (CSU) to join us in a hybrid role based out of our office in Seattle, Washington. The individual assuming this role will own the AI use case portfolio for McKinstry's Corporate Services functions: Legal, Finance/FP\&A, HR, Marketing, and IT. They will also be accountable for connecting the pockets of active experimentation already underway in each function into a governed, sequenced program that delivers measurable outcomes within the current fiscal year. This role translates the enterprise AI foundation into operational value for corporate functions, operating at the boundary between what the program can build and what professional back\-office teams can adopt. This role is successful when CSU functions are measurably more efficient because of AI; fewer hours on contract review, faster FP\&A analysis and automated workflows that free skilled professionals for higher\-value work.
Responsibilities include:
- Owns the AI strategy and roadmap for Corporate Services — defining where intelligence creates the most value across Legal, Finance, HR, Marketing, and IT workflows, sequencing how McKinstry gets there, and ensuring AI investments build on the enterprise foundation rather than proliferating disconnected tools.
- Owns the AI use case pipeline for CSU: intake, evaluation, prioritization, and the GO/NO\-GO decision; ensuring the contract review pilot, CBA orchestrator, AP invoice processing, HR screening, and FP\&A early warning initiatives move from active experimentation into governed delivery without accumulating in a backlog.
- Builds the AI team's own operating model as the proof case for CSU, bringing our own team's operations to a standard worth copying, maintaining the single source of truth for the team's decisions, metrics and documents and developing that operating model into the template the role then takes to the other Corporate Services functions. We improve our own operations first, then lead the operations transformation for CSU.
- Runs the business review and performance measurement cycle. Prepares and runs the Monthly and Quarterly Business Reviews (MBRs and QBRs), runs the OKR cycle and measures the KPIs and captures the underlying detail so the numbers are defined, current and traceable. Maintains the settled\-decisions record and the single open\-items tracker and drives every commitment to a named owner and close date.
- Personally leads high\-priority AI pilots end\-to\-end including the Contract/SOW review automation pilot as the anchor; from scoping and stakeholder alignment through execution, measurement, and the decision to scale or stop, owning outcome metrics for every pilot in the portfolio.
- Builds and runs the AI champion network within CSU to include recruiting, developing, and connecting legal counsel, finance analysts, HR business partners, and IT leads who beta test tools, give real feedback, and drive peer adoption across functions.
- Works directly with CSU function heads to identify where AI creates the most value in their workflows, co\-designs solutions that fit how professional teams actually operate, and manages the particular change management challenge of gaining adoption among skeptical, risk\-conscious professional audiences.
- Represents CSU Priorities in McKinstry’s AI governance forums, coordinates with the enterprise hub to stay aligned on foundation work and escalates when a decision requirs executive authority; including decisions about data access, confidentiality, and responsible AI guardrails for Legal and HR use cases.
What You Need to Succeed at McKinstry:
- 10\+ years working in corporate operations, shared services, legal operations, finance operations or HR operations preference of partnerships with at least two corporate functions to understand where the manual work is and why it persists.
- Bachelor’s degree in related fields. Relevant experience may be substituted for a degree.
- Demonstrated experience leading a digital, automation, or AI initiative inside a corporate function as the person accountable for whether it worked.
- Comfortable with the particular change management challenge of corporate professional audiences: lawyers, finance analysts, and HR business partners are risk\-conscious, skeptical of tools that touch their work, and require a different adoption approach than field operations teams.
- Able to translate clearly between corporate function workflows and technology: can explain a document processing pipeline constraint to a legal counsel and a contract review problem to an AI engineer with equal clarity.
- Track record building adoption for tools in environments where resistance is professional, articulate, and legitimate — understands that responsible AI guardrails and transparent design are prerequisites for Legal and HR adoption, not obstacles to it.
- Strong written communication: can write a one\-page use case evaluation for a function head, a pilot scope document an AI engineer can build from, and an executive update that doesn't require editing.
- Working familiarity with the responsible AI considerations specific to corporate function use cases: citation requirements for legal outputs, bias risk in HR screening, data classification for financial AI, and confidentiality for any use case touching sensitive corporate data.
- Proven ability to manage cross\-functional stakeholders across business units, technology functions, and external partners in a matrixed organization.
- Proven ability to manage cross\-functional stakeholders across field operations, technology functions, and external service partners in a matrixed organization.
- PMP, Six Sigma, or Lean credentials for process improvement are preferred
PeopleFirst Benefits
When it comes to the basics, we have you covered:
- Competitive pay
- 401(k) with employer match and profit\-sharing plan
- Paid time off and holidays
- Comprehensive medical, prescription, dental, and vision with low or zero deductible options and low out of pocket maximums
People come first at McKinstry, and we go beyond the basic benefits with:
- Family formation benefits, including adoption and IVF assistance
- Up to 16 weeks paid parental leave
- Transgender inclusive benefits
- Commuter benefits
- Pet insurance
- “Building Good” paid community service time
- Learning and advancement opportunities via McKinstry University
- McKinstry Moves onsite gyms or reimbursement for remote workers
See benefit plan documents for complete details.
If you’re driven by our vision to build a thriving planet together, McKinstry is the place to build your career.
T*he pay range for this position is $165,800 \- $252,100 per year; however, base pay offered may vary depending on job\-related knowledge, skills, and experience. Base pay information is based on market location. A bonus may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, dependent on the position offered.*
*The McKinstry group of companies are equal opportunity employers. We are committed to providing equal employment opportunities to all employees and qualified applicants without regard to sex, gender identity, sexual orientation, age, race, color, creed, marital status, national origin, disability, veteran status, genetic information or any other basis protected by law. This policy applies to all terms and conditions of employment including, but not limited to employment, advancement, assignment, and training. This commitment to Equal Employment Opportunity is made equally as a social responsibility and as an economic and business necessity.*
*McKinstry is a drug\-free workplace. Employment is**contingent upon successfully passing a pre\-employment drug and alcohol test, complying with the requirements of the Immigration Reform and Control Act and a Confidentiality Agreement, in addition to successful outcomes of background and reference checks.*
*Applicants for this role will only be considered if they possess current US Work Authorization, and do not require employer\-sponsored VISA support to begin or remain in this role.*
\#LI\-DL1
Salary Context
This $165K-$252K 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 McKinstry, 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. Disclosed range: $165K to $252K.
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.
McKinstry AI Hiring
McKinstry has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $252K - $252K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national 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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