Interested in this AI/ML Engineer role at Parametrix, Inc.?
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About This Role
### About Parametrix
Parametrix is a 100% employee\-owned consulting firm helping clients deliver resilient infrastructure, vibrant and livable communities, and environmentally sustainable solutions. We are a team of professional construction managers, engineers, management consultants, planners, scientists, and surveyors dedicated to delivering outstanding client service. We build lasting partnerships and collaborate with our clients to envision possibilities and create tomorrow, together.
Recognized as a top workplace, we're committed to a culture that values work\-life balance, professional growth, and the well\-being of our people.
### About the Opportunity
Parametrix is seeking a Senior Applied AI Engineer to join our Digital Services team. In this role, you'll design, build, and deliver practical AI solutions that help improve the way our employees work. Working on our established Azure\-based AI platform, you'll develop AI agents, applications, and reusable digital assets while partnering with teams across the firm to drive successful adoption of enterprise AI capabilities.
This is a hands\-on engineering role for someone who enjoys translating business opportunities into scalable, secure AI solutions and helping people embrace new technologies. You'll collaborate with technical and business stakeholders to identify high\-value use cases, deliver production\-ready applications, and support the successful integration of AI into day\-to\-day workflows.
This position offers flexible work arrangements, including hybrid work from any Parametrix office location or remote work from within the United States.
Work Authorization: Applicants must be legally authorized to work in the United States at the time of application. Parametrix does not sponsor employment visas for this position now or in the future.
Your Role on the Team:
Design and Build AI Solutions
- Design, develop, and deploy AI agents and applications using Claude, ChatGPT, Azure AI Foundry, and Microsoft Copilot.
- Create and maintain reusable AI assets, including custom GPTs, Claude skills, and Copilot agents, that enable employees to effectively leverage AI in their daily work.
- Integrate AI solutions with enterprise systems, Microsoft 365, REST APIs, and business data to deliver secure, reliable, and context\-aware experiences.
- Develop production\-ready applications with a focus on code quality, testing, documentation, reliability, and maintainability.
- Implement validation, evaluation, and monitoring processes that ensure AI solutions produce accurate and dependable results.
Collaborate and Support Adoption
- Partner with teams across the firm to identify opportunities where AI can improve business processes and operational efficiency.
- Work with technical and business stakeholders to understand their needs and translate them into clear solution requirements.
- Create documentation, training materials, and best practices that support successful implementation and ongoing use of AI solutions.
- Ensure solutions align with the firm's security, governance, and data management standards.
- Contribute to the firm's Applied AI Community of Practice by sharing knowledge, staying current with emerging AI technologies, and recommending practical innovations.
What You Bring to the Team:
We're looking for someone with a combination of strong software engineering skills, applied AI experience, and the ability to collaborate effectively across technical and business teams. Strong software engineering is the foundation for this role, and the AI and platform skills below build on it. We recognize candidates may bring deeper expertise in some areas than others.
- Experience designing, developing, testing, and deploying production Python using modern development practices and Git\-based workflows.
- Demonstrated success building AI\-powered applications, including prompt engineering, agent workflows, and large language model (LLM) integrations.
- Hands\-on experience with one or more leading AI platforms, including Claude, ChatGPT, Azure AI Foundry, or Microsoft Copilot.
- Ability to develop reusable AI solutions, such as custom GPTs, Copilot agents, or similar AI assets that support business users.
- Experience integrating applications with REST APIs, enterprise systems, and business data sources.
- Familiarity with modern AI development concepts such as retrieval\-augmented generation (RAG), vector search, Model Context Protocol (MCP), or related technologies.
- Ability to collaborate with business stakeholders to identify needs, implement solutions, and support organizational adoption.
- Understanding of security, governance, and responsible AI practices within enterprise environments.
Preferred Qualifications
- Microsoft Azure AI Engineer Associate certification or comparable AI\-related certification.
- Demonstrated experience building AI agents in C\#/.NET, particularly using Microsoft Agent Framework or Semantic Kernel on Azure.
- Proficiency with React, TypeScript, or other modern front\-end development frameworks.
- Working knowledge of Microsoft Copilot Studio, Power Platform, or other low\-code development platforms.
- Ability to work with SQL and enterprise business data.
- Background in the architecture, engineering, and construction (AEC) industry or another professional services environment.
- Familiarity with AI agent orchestration frameworks and related technologies.
### Why Join Parametrix?
Employee Ownership \& Great Benefits: As a 100% employee\-owned company, you will share in and contribute to Parametrix's success. You will earn stock in your Employee Stock Ownership Plan (ESOP) account and be an important contributor to our collective achievements.
Our Benefits include:
- Comprehensive Healthcare (medical, dental, vision, short\- \& long\-term disability insurance)
- Employee Stock Ownership Plan (financial profit sharing)
- Performance\-based bonuses
- 401(k) Plan
- Paid Time Off (both vacation \& sick/wellness time accruals)
- Paid Holidays
- Parental Bonding Leave
Exciting, Award\-Winning Project Work: Our work earns recognition for its innovation and positive impact on communities, giving you the opportunity to contribute to projects that make a difference. Learn more at https://www.parametrix.com/our\-work/
Flexible Work Arrangements: We understand the importance of work\-life balance and offer flexible work arrangements to support our employee\-owners' diverse needs. Whether it is hybrid, remote, or in\-office, we provide options that allow you to work in a way that best suits your lifestyle while staying connected and engaged with your team.
Compensation is determined by factors such as education, experience, location, and role. As employee\-owners, we are eligible for performance\-based bonuses and our salaries are reviewed annually. We value transparency and look forward to discussing our compensation structure.
### Our Commitment to You
Parametrix is committed to being an inclusive workplace, where team members of all backgrounds and experiences are welcome. As an equal opportunity employer, it is our policy and culture to provide opportunities to all persons based on merit and fitness to perform job duties. Employment decisions are based solely on business needs, job requirements, and individual qualifications, without regard to race, color, religion, creed, national or ethnic origin, sex (including pregnancy), sexual orientation, gender identity or expression, marital status, religion, age (40 or older, as protected under the ADEA), disability (including physical, mental, or sensory), genetic information (including testing and characteristics), protected veteran status, or any other status or characteristic protected by applicable federal, state, or local laws or regulations.
At Parametrix, we are dedicated to encouraging an inclusive and accessible workplace. If you need any accommodations during the application or interview process, please let us know, and we will work with you to ensure your needs are met. We welcome and encourage candidates from all backgrounds to apply.
Salary Context
This $165K-$195K 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 Parametrix, Inc., 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $165K to $195K.
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.
Parametrix, Inc. AI Hiring
Parametrix, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $195K - $195K.
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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