Interested in this AI/ML Engineer role at Sellen Construction?
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About This Role
Join us in Seattle and help shape how Sellen builds with data and AI.
Sellen Construction is looking for a Data and AI Engineer to help automate manual work across the business. Reporting to the Director of Data Platforms, this person will gather requirements from business stakeholders and build trusted, well-governed solutions. About 40% of the role is building AI agents and automated workflows, mainly in the Microsoft stack (Copilot Studio, Power Automate, Azure AI Foundry). Examples include document intake and routing, RFI and submittal triage, and meeting summaries. Another 40% is data engineering: building pipelines out of Vista, Procore, UKG and SharePoint, and maintaining Sellen’s analytics layer and semantic models in Microsoft Fabric. The remaining time goes to data quality and governance, including CUI/CMMC boundaries, and to supporting the Data Analyst with certified datasets.
**The position is located in South Lake Union full-time.
Your Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent experience.
- 5+ years of relevant work experience in data engineering, software development, or systems integration, with exposure to the requirements, processes, and functions of general contracting preferred.
OR
- 3+ years of experience with direct responsibility for building and maintaining data pipelines, integrations, or automation solutions, including: + Designing and building ETL/ELT pipelines and data integrations. + SQL and Python (or a similar language) for data transformation and automation. + Integrating enterprise systems through REST APIs (Procore, Viewpoint Vista, UKG, or similar ERP/project management platforms preferred). + Cloud data platforms such as Microsoft Fabric, Azure Data Factory, Azure SQL, or Synapse. + Data modeling, data quality, and governance practices. + Building AI-enabled automations or agents using LLM platforms, Power Automate, or similar tools. + Version control (Git) and deployment practices for production systems.
LICENSES / CERTIFICATIONS
- One or more of the following, or equivalent (preferred):
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Microsoft Certified: Fabric Analytics Engineer Associate (DP-600).
- Microsoft Certified: Azure Data Fundamentals (DP-900).
- Microsoft Certified: AI Business Professional (AB-730).
KNOWLEDGE
- Strong working knowledge of construction operations practices, processes, and commercial construction project context (preferred).
- Strong working knowledge of the information required by preconstruction and construction operations in day-to-day decision-making and in the creation of schedules, estimates, and budgets (preferred).
- SQL and DAX, including working with semantic models and star schemas.
- Microsoft Fabric and Power BI Service (preferred).
- Data governance concepts, including data ownership, data quality, metadata, and data classification.
- Practical use of AI tools for analysis, and awareness of their limits (preferred).
- Agile development framework.
SKILLS
- Process improvement through automation: Finds manual, repetitive workflows across operations, preconstruction, and the field, and replaces them with reliable automated solutions.
- Project planning and delivery: Breaks pipeline, integration, and agent projects into milestones, ships working pieces in stages, and manages priorities across competing requests.
- Engineering judgment: Designs data pipelines and AI workflows that are maintainable, monitored, and secure. Decides when to build, when to buy, and when not to automate.
- Problem solving: Troubleshoots data quality issues, failed integrations, and unexpected model or agent behavior by finding the root cause instead of applying patches.
- Ownership and reliability: Treats production pipelines and automations as services. Owns them end to end, from monitoring and alerting through fixes.
- Responsible AI and data governance: Builds with security, access controls, and compliance in mind, including handling CUI and sensitive project data appropriately.
- Communication skills:
+ Technical documentation: Writes clear runbooks, data definitions, and architecture notes so others can support and extend the work. + Requirements and collaboration: Turns loosely defined business needs from non-technical stakeholders into working solutions, and partners closely with the data analyst, who uses the data this role produces.
ABILITIES
- Demonstrate critical and holistic thinking.
- Ability to communicate comfortably and effectively with new and diverse groups of people.
- Ability to influence others and lead change.
- Ability to critically analyze complex and varied information to make recommendations.
- Ability to leverage technology to facilitate communication and collaboration.
- Comfortable collaborating across functions to identify data analytics problems and execute solutions with technical rigor and data-driven insights.
The full salary range for this position is $118,000 to $145,000. New hires typically start between $118,000 and $138,000*, depending on direct professional experience and qualifications. The bonus target for this role is* 5%** of annual base earnings. Learn more about Sellen's Fair Pay Workplace certification, our bonus plan, and other rewards and benefit programs here (https://www.sellen.com/wp-content/uploads/2024-Sellen-Benefits-at-a-Glance.pdf).
About Sellen
Sellen's purpose is to improve the lives of those around us - as builders, partners and neighbors. Our goal is to hire the best talent and develop the strongest project teams in the industry. If you bring strong skills, thrive in a project environment, and enjoy team spirit and collaboration, please introduce yourself.
Our Benefits At Sellen, we provide our team members with an environment that promises safety, stability, and values mental health. Competitive benefits including 401(k) with up to 9.5% employer contribution, health insurance, health savings contributions, tuition benefits, and more. Sellen is the first general contractor in the country to receive the Fair Pay Workplace Certification, which ensures we are paying our people equitable across gender and race.
Our Culture & Reputation
Doing things the "Sellen Way" means being good at our craft, doing what's right, and caring for one another. As a local, employee-owned company, based in the Pacific Northwest for 80 years, we live, work and raise our families here, and Sellen is committed to actively supporting our community and our people.
Our DEI Commitment
We recognize the impact Sellen can make in creating a more diverse A/E/C industry. We envision a diverse workforce where people of color, women, LGBTQIA+, and other underrepresented and historically underestimated populations are reflected at all levels.
Sellen is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $118K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sellen 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($131K) sits 38% below the category median. Disclosed range: $118K to $145K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Sellen Construction AI Hiring
Sellen Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $145K - $145K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,095 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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