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About This Role
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
McKinstry is looking for a Senior Program Manager – AI and Automation to join our Offsite Manufacturing division in Seattle, WA. The Senior Program Manager – AI and Automation owns the pipeline that converts emerging manufacturing automation and artificial intelligence capability into deployed, value-generating solutions for the business. The role discovers and maintains awareness of business needs across the business unit, researches and evaluates candidate technologies, builds the value proposition and business case, and proves solutions through structured pilots. Automation solutions are refined and coordinated with Manufacturing Engineering for implementation; AI applications are implemented by this role directly alongside the business function they affect. Additional responsibilities may include:
Business Need Discovery and Demand Management
- Maintain a living, prioritized view of AI and automation opportunities across the business unit by interviewing leaders and embedding with production, engineering, supply chain, quality, project management, finance, and administrative teams as needed.
- Conduct structured discovery (e.g. process observation, time and data studies, stakeholder interviews) to separate real constraints from symptoms and size the opportunity before any solution is proposed.
- Translate business pain into problem statements and requirements that vendors, engineers, and internal stakeholders can act on.
- Track the business unit’s strategic plan, growth targets, and live production commitments so the technology pipeline is sequenced against what the business needs next, not against what is newest.
- Work with Manufacturing Engineering to determine current bottlenecks in the production process, and seek out solutions that could increase throughput through use of AI and/or Automation.
Technology Research and Solution Evaluation
- Scout and maintain a current, credible market view across manufacturing automation, to include robotics, material handling, machine vision, sensors, controls, and across enterprise and applied AI platforms, vendors, and integrators.
- Run structured evaluations: requirements definition, RFI/RFP, vendor demonstrations, reference checks, technical feasibility assessment, and total cost of ownership.
- Assess integration fit against existing systems of record (ERP, PLM, MES, document control) and coordinate with Enterprise IT, Data, Cybersecurity, and Legal on architecture, data handling, licensing, and security review.
- Distinguish deployable capability from immature capability and report that distinction plainly, including when the recommendation is to wait or to do nothing.
- Serve as the Modular Products representative to the Applied Frontier Technology team and enterprise technology teams — communicating the business unit's automation and AI roadmap, ingesting their recommendations and emerging-capability guidance, and feeding those inputs into the evaluation pipeline.
Value Proposition and Business Case
- Build the business case for each candidate solution – quantified benefit, cost, payback, ROI, risk, resourcing, and the assumptions each depends on.
- Establish baseline metrics before pilot so benefit claims are measurable after deployment rather than asserted.
- Present recommendations and funding requests to business unit and enterprise leadership, and secure an accountable sponsor in the business function that will own the outcome.
- Maintain the portfolio view of what sits in discovery, evaluation, pilot, implementation, and sustainment, and what each is worth.
Pilot Design, Execution, and Refinement
- Design pilots with explicit hypotheses, success criteria, decision gates, budget, and a defined end date.
- Lead pilot execution on the plant floor and within the business function, managing vendors, internal resources, schedule, and safety and quality risk.
- Refine solution configuration, workflow, and supporting standard work based on pilot evidence, and iterate, scale, or terminate on that evidence.
- Document pilot outcomes so the organization compounds what it learns.
Implementation and Adoption
- Transition proven automation solutions to Manufacturing Engineering for execution — with complete requirements, vendor relationships, technical documentation, integration plan, and validated business case — while retaining accountability for the solution through installation, startup, and post-implementation support. Coordination with Manufacturing Engineering is a shared execution model, not a handoff of ownership: this role stays on the hook until the solution is running, adopted, and delivering the committed business case, and remains the escalation point for performance issues, rework, and optimization after go-live.
- Own implementation of AI applications end to end alongside the impacted business function, covering data readiness, configuration, integration, testing, training, cutover, and hypercare.
- Coordinate governance with Enterprise teams for deployed AI applications – data quality and access, model monitoring and drift, human review, and responsible-use guardrails aligned to enterprise policy.
- Track realized benefit against the business case after deployment and report variance honestly.
Change Management and Capability Building
- Build organizational readiness for automation and AI, addressing workforce impact, role change, and skills development directly and early rather than after deployment.
- Train and coach production and business teams on deployed solutions so operating capability transfers to the team that owns the work.
- Communicate the technology roadmap, pilot results, and realized value to mixed executive, engineering, operations, and craft audiences.
- Report out to the Applied Frontier Technology team on pipeline progress, pilot outcomes, deployed solutions, and realized value, maintaining a two-way channel between Modular Products and enterprise frontier technology efforts.
What You Need To Succeed at McKinstry:
- Bachelor’s Degree required (industrial, mechanical, electrical, manufacturing, or computer engineering, computer science, or operations management preferred).
- Minimum eight (8) years of team and/or program management experience required.
- Demonstrated experience carrying technology from evaluation through pilot to production deployment in a manufacturing, industrialized construction, or offsite fabrication environment.
- Working knowledge of manufacturing automation technologies such as industrial robotics, automated material handling, machine vision, sensors, and PLC/controls integration.
- Working knowledge of applied artificial intelligence and machine learning, including a practical understanding of where current capability performs reliably and where it does not.
- Demonstrated ability to build and defend a quantified business case, including benefit modeling, ROI, payback, and total cost of ownership.
- Working knowledge of process mapping and lean methods – standard work, kaizen, A3 problem solving, root cause analysis.
- Demonstrated ability to collaborate with and influence people at all levels across the organization required.
- Excellent facilitation skills and the ability to lead diverse, cross-functional project teams required.
- Ability to operate effectively in a high-growth environment where technology strategy is authored in parallel with live production commitments.
- Strongly Preferred: vendor, integrator, and capital equipment procurement experience; data platform and integration fundamentals (APIs, data pipelines, structured and unstructured data); AI governance, security, and privacy practice; PMI certification; LEAN Construction; engineer-to-order or high-mix production experience.
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.
The pay range for this position is $110,790 - $190,700 per year; however, base pay offered may vary depending on job-related knowledge, skills, and experience. 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. Base pay information is based on market location.
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-IF1
Salary Context
This $110K-$190K 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 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 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $110K to $190K.
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.
McKinstry AI Hiring
McKinstry has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $169K - $190K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% 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 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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