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About This Role
Drive Quality Innovation as our Next QA Manager (AI & Automation)!
Ready to pioneer the future of AI-driven software testing and transform how engineering teams deliver quality at scale? Trimble is looking for an innovative QA Manager to lead our pod quality strategy, leverage cutting-edge AI tools, and hands-on maintain automation frameworks in a modern, collaborative environment!
About Us: Trimble is empowering stakeholders through planning, design, construction and the entire asset lifecycle. The company’s innovative and connected ecosystem of solutions improves coordination and collaboration between teams, phases and processes. Automating work and transforming workflows, Trimble is enabling teams to deliver with confidence at every turn. For more information, visit: construction.trimble.com.
What Makes This Role Great: In this role, you will redefine product quality by shifting from traditional testing to an AI-powered, developer-owned quality model. You will act as the master strategist and technical guide for the pod, evaluating advanced AI tools, building automation, and ensuring top-tier software reliability.
- Architect and enforce pod-wide quality strategy while personally authoring test automation using tools like Playwright, Python, C#, or Java.
- Pioneer testing methodologies for embedded AI components, evaluating LLMs, computer vision, and speech recognition while managing AI agents and mitigating bias.
- Evaluate, recommend, and train cross-functional engineering teams on modern AI-driven test tools and automation techniques.
- Orchestrate end-to-end operational workflows, including regressions, release management, and cloud or virtualization test environments.
- Drive data-backed decisions by tracking quality metrics, quantifying coverage gaps, and performing root cause analysis on production incidents to continually improve processes.
Essential Skills & Experience
- Bachelor's degree in Computer Science or a related technical field, with 10+ years of experience leading software quality strategy and managing global teams.
- Proven ability to write, maintain, and execute test automation scripts personally in Playwright, C#, Python, or Java.
- Strong foundation in core AI concepts (LLM, NLP, ML) with hands-on experience testing AI components, managing AI agents, and using MCP servers.
- Solid background in team-level release management, regression strategy, and virtualization or cloud test environments.
- Outstanding coaching and communication skills to influence quality mindsets and convey technical risk clearly across product and engineering stakeholders.
Bonus Points For:
- Experience in global work environments or construction back-office functions and accounting.
- Familiarity with performance, security, chatbot, and embedded AI testing techniques.
- Hands-on proficiency with Jira and C#.
Logistics:
Location: Lake Oswego, Portland, Oregon (4 Days In Office)
Travel Requirement: 10%
Why You'll Love Working With Us: At Trimble, we're not just a company that "does good"—we are a team dedicated to making a tangible, positive Real-World Impact. We build innovative solutions designed to solve the world's most critical challenges. From construction sites to transportation hubs, our work tangibly improves how people live, build, move, and grow.
- You'll work on projects that truly matter: Our purpose-driven culture means you'll be helping to build and deliver solutions that make work faster, safer, and more sustainable for millions of people worldwide. Our impact is tangible, from connected machines that save fuel to data-driven insights that reduce waste.
- Collaborate with like-minded people: Our strong internal culture is a "hidden gem." You will work with a collaborative, supportive team that shares your purpose and fosters a genuine sense of belonging. We're a company of "visionary pragmatists" who think boldly and build things that work.
- Be an owner: Trimble thrives on individuals who take initiative and embrace ownership. You'll find an entrepreneurial spirit where success is often "self-authored," empowering proactive "doers."
QA Manager, Software Quality Assurance, AI Testing, Test Automation, Playwright, Python, C#, LLM Testing, Model Context Protocol, Quality Engineering, Release Management, Lake Oswego Jobs
Compensation: Trimble provides the following compensation range and general description of other compensation and benefits that it in good faith believes it might pay and/or offer for this position. This compensation range is based on a full time schedule. Trimble reserves the right to ultimately pay more or less than the posted range and offer additional benefits and other compensation, depending on circumstances not related to an applicant’s sex or other status protected by local, state, or federal law.
Hiring Range
$170,600.00–$234,600.00Pay Rate Type
SalaryBonus Eligible?
YesCommission Eligible?
No Benefits: Trimble offers comprehensive core benefits that include Medical, Dental, Vision, Life, Disability, Time off plans and retirement plans. Most of our businesses also offer tax savings plans for health, dependent care and commuter expenses as well as Paid Parental Leave and Employee Stock Purchase Plan. If this position is identified above as commission- or bonus-eligible, the terms of the commission plan or discretionary bonus plan for which you are eligible will be provided following the employee start date.
How to Apply: Please submit an online application for this position by clicking on the ‘Apply Now’ button located in this posting.
Application Deadline: Trimble accepts applications on an ongoing basis until the position is filled.
At Trimble, we are committed to fostering a diverse, inclusive, and equitable workplace where everyone can thrive. Guided by our core values—Belong, Innovate, and Grow—we embrace and celebrate differences, knowing they make us stronger and more innovative. We are proud to be an equal opportunity employer, welcoming individuals of all backgrounds and advancing opportunities while embracing race, color, gender identity, sexual orientation, religion, disability, veteran status, or any other protected and diverse characteristic. We are committed to offering our candidates and employees with disabilities or sincerely held religious beliefs the ability to seek reasonable accommodations in accordance with applicable law and/or where it would not constitute undue hardship for Trimble. For more, please see Trimble's Code of Business Conduct and Ethics at https://investor.trimble.com, under “Corporate Governance.”
Our mission to transform the way the world works starts with transforming how we work together. By actively listening, asking questions, and taking intentional actions, we cultivate a culture that provides equitable opportunities for everyone to contribute and grow.
Candidate Privacy Notices by Country
If you need assistance or would like to request an accommodation in connection with the application process, please contact [email protected].
Salary Context
This $170K-$234K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Trimble, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $170K to $234K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Trimble AI Hiring
Trimble has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lake Oswego, OR, US. Compensation range: $234K - $234K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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