Interested in this AI/ML Engineer role at Trane Technologies?
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About This Role
Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane® and Thermo King, sustainability is not just how we do business—it is our business. Do you dare to look at the world's challenges and see impactful possibilities? Do you want to contribute to making a better future? If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world.
Learn about our benefits designed for you to Thrive at work and at home.
We boldly go.
Where is the work:
Monday to Thursday, work onsite with your colleagues. Fridays, choose your work location, balancing what your work requires. Agentic Software Test Engineer — TRACE
At Trane Technologies® we Challenge Possible. Our brands – including Trane® and Thermo King® – create access to cooling and comfort in buildings and homes, transport and protect food and perishables, connect customers to elevated performance with less environmental impact, dramatically reduce energy demands and carbon emissions, and innovate with a better world in mind. We boldly challenge what's possible for a sustainable world.
The Role
The Agentic Software Test Engineer — TRACE sits on the Customer Driven Solutions (C.D.S.) Team and runs the agentic testing organization for TRACE — the building energy simulation platform that powers HVAC load calculations, equipment sizing, and ASHRAE\-compliant reporting for engineers worldwide.
You direct a team of AI testing agents across a multi\-repo .NET / Blazor codebase, working shoulder\-to\-shoulder with the TRACE software developers as their dedicated QA partner. You're the captain of the agent crew and the quality voice the developers trust — coaching the agents, raising the bar, and putting your name on the work that ships.
This is a hands\-on engineering seat; you direct the agent crew; you partner with the humans.
What You'll Do* Direct the agentic testing team — define agent personas, dispatch patterns, and human\-in\-the\-loop checkpoints for QA across TRACE.
- Author the prompts, context contracts, and knowledge bases that drive the agents through test plan generation, regression authoring, defect triage, exploratory testing, and accessibility sweeps.
- Build evaluation harnesses that measure agent output quality — false\-positive rates on bug reports, regression coverage drift, flaky\-test detection, eval\-vs\-production divergence — and tighten the loop sprint over sprint.
- Curate what the agents produce — test plans, automated scripts, exploratory findings, regression results — and ship the best of it into the engineering pipeline with your stamp on it.
- Hunt bugs through manual, automated, and agent\-driven methods, and verify every agent\-surfaced finding before it reaches the development team.
- Own TRACE's quality posture as the SME on HVAC simulation correctness, ASHRAE compliance reporting, EnergyPlus integration behavior, and cross\-repo regression risk.
- Partner with developers and Product Owners on test coverage decisions, release readiness, and quality strategy.
- Run regression, functional, and data tests across the .NET 10\.0 backend, Blazor frontend, PostgreSQL schema, and EnergyPlus calculation engine.
- Design adversarial verification workflows — parallel agents that try to refute a test result before it's trusted.
- Curate the agent learning loop — capture recurring failure patterns, encode them into spec rules, and feed them back into agent knowledge bases so the same defect class is caught earlier next sprint.
- Translate customer\-impact reports into agent\-runnable test scenarios and explain quality posture to non\-technical stakeholders without hand\-waving.
What You Bring
Required* 5\+ years in software quality assurance, with a real track record of shipping tested software.
- 1\+ year hands\-on with LLM\-based agents, AI coding assistants, or multi\-agent systems in a production or near\-production workflow.
- Strong test\-design instincts across unit, integration, system, functional, and exploratory layers; comfortable owning test plans, defect lifecycle, regression strategy, and release gating.
- Hands\-on experience with AI coding agents (Claude Code, Cursor, Copilot Workspace, or equivalent) and the Anthropic / OpenAI APIs.
- Working knowledge of prompt engineering, structured outputs (JSON Schema), tool use, and multi\-agent orchestration (parallel/pipeline/judge\-panel/adversarial\-verify).
- Comfort designing evals and treating eval results as a first\-class quality metric.
- Bias toward human\-in\-the\-loop verification — never trusting agent output blindly.
- Ability to read and modify production code in C\#, Python, or JavaScript — not just test code.
- Experience with GitHub Actions testing pipelines.
- Working knowledge of Jira (or equivalent) and testing tools like Playwright, BUnit, xUnit / NUnit.
- Strong written and oral communication; able to explain agent behavior, eval results, and quality posture in plain language.
Preferred* Bachelor's Degree in Engineering, Computer Science, or related discipline (or equivalent experience — we care more about what you've shipped than where you studied).
- Familiarity with C\#, .NET 10\.0, Blazor, ASP.NET Core, Razor, plus the usual web stack (JavaScript / TypeScript / CSS / HTML).
- Working knowledge of PostgreSQL and EF Core, including database\-aware test design (cascade\-delete chains, FK integrity, schema migration testing).
- Background in building energy simulation, EnergyPlus, ASHRAE 140 software testing, ASHRAE 90\.1 / 62\.1 / 169 compliance reporting, or HVAC equipment sizing workflows.
- Agile / Scrum experience.
Working Style
This is an on\-site role so you can pair tightly with the TRACE engineering team and the agent crew you're directing. Expect a fast feedback loop, candid technical conversation, and a team that takes both craft and outcomes seriously.
We offer competitive compensation and comprehensive benefits and programs that help our employees thrive in both their professional and personal lives. We are proud of our winning culture which is inclusive and respectful at its core. We share a passion for serving customers, caring for others, and boldly challenging what's possible for a sustainable world.
We are committed to achieving workforce diversity reflective of our communities. We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.
Annual Base Salary Range or Hourly Base Pay Range:
$97,406\.66 \- $135,834\.99Compensation Type:
SalaryIncentive Eligible:
NoSales Commission Eligible:
NoDisclaimer: We strive to provide competitive compensation for this position, tailored to a variety of factors. The actual compensation will depend on elements such as seniority, merit, geographic location, education, experience, travel requirements, and union designation. Our compensation range is generally based on the national average for the country. Additionally, benefits may vary depending on the region, business alignment, union involvement, and employee status.
Thrive at work and at home:
- Benefits kick in on DAY ONE for you and your family, including health insurance and holistic wellness programs that include generous incentives – WE DARE TO CARE!
- Family building benefits include fertility coverage and adoption/surrogacy assistance.
- 401K match up to 6%, plus an additional 2% core contribution \= up to 8% company contribution.
- Paid time off includes 15 vacation days, 9 paid holidays, 3 floating holidays, sick leave, and additional options to support volunteer and parental leave.
- Educational and training opportunities through company programs along with tuition assistance and student debt support.
Disclaimer: Benefit offerings may vary depending on Collective Bargaining Agreements and local/state regulations.
Safety Sensitive Role:
No
The company designates certain roles as Safety Sensitive. Safety Sensitive roles may require that you pass additional drug screening.
We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.
Salary Context
This $97K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Trane Technologies, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($116K) sits 36% below the category median. Disclosed range: $97K to $135K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Trane Technologies AI Hiring
Trane Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in La Crosse, WI, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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