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About StarRez StarRez is the global leader in student housing software, providing innovative solutions for on and off-campus housing management, resident wellness and experience, and revenue generation. Trusted by 1,400+ clients across 25+ countries, StarRez supports more than 4 million beds annually with its user-friendly, all-in-one platform, delivering seamless experiences for students and administrators. With offices in the United States, Australia, the UK, and India, StarRez blends the robust capabilities of a global organization with the personalized care and service of a trusted partner. The Role: We are looking for a Senior Quality Engineer - AI to help shape how StarRez evaluates, validates, and improves AI-powered product experiences. You will play a pivotal role in elevating our quality practices for AI-powered product experiences and tooling. You will be a product expert within our engineering organization, deeply understanding workflows and customer outcomes. This role will balance hands-on individual contributor responsibilities with leading, influencing, and coaching your peers. You’re someone who is passionate about seeing systems holistically and is eager to drive the highest standards of accuracy, safety, usefulness, and reliability. You will help define what "good" AI output means for StarRez, build repeatable evaluation systems, coach reviewers and subject matter experts, and turn subjective feedback into measurable product improvement. You will work at the intersection of AI engineering, product, and domain knowledge — partnering with engineers, product managers, and SMEs to raise the bar on how StarRez evaluates, monitors, and improves AI tools. Key Responsibilities: Lead or contributed to end-to-end AI quality and evaluation strategy for product experiences involving LLMs, RAG, prompts, tool-use, or agent workflows. Provided quality-focused input during ticket grooming, discovery, and feature discussions, with clear guidance on testability, observability, scalability, reliability, product resilience, customer usability, and operational readiness. Championed shift-left quality practices by influencing or mentoring developers and cross-functional partners on evaluation-driven development, clear acceptance criteria, early validation, and fast feedback loops. Coached or calibrated human reviewers, subject matter experts, or product teams by running calibration sessions, reviewing patterns, improving rubric clarity, and converting domain knowledge into reliable evaluation criteria. Contributed to the evolution of development or quality pipelines, including automated and human evaluation workflows, regression monitoring after model upgrades, prompt changes, RAG updates, or agent workflow changes. Define practical AI quality standards, rubrics, and evaluation criteria that help teams assess AI outputs consistently across StarRez product workflows. Partner with engineering and product teams to identify AI quality risks early, including risks related to grounding, hallucination, tool-use behaviour, retrieval quality, customer impact, and safe escalation. Build or support repeatable evaluation approaches that help teams compare prompt, model, RAG, data, or workflow changes before release. Translate AI failure patterns into actionable product and engineering improvements, such as prompt changes, retrieval improvements, source-data fixes, tool safeguards, UX changes, or escalation-flow updates. Use metrics, reviewer feedback, quality trends, and operational signals to support release readiness and ongoing product improvement. Required Experience: Quality Assurance or Quality Engineering experience in a SaaS B2B or B2B2C company. Proven track record as a senior individual contributor with a strong quality mindset and the ability to influence technical and product direction. Excellent technical communication and collaboration skills, with the ability to guide engineering peers, product partners, and non-technical stakeholders through clear quality insights. Data-driven approach to quality management, with experience using metrics, dashboards, trend analysis, reviewer agreement, or operational reporting to guide decisions. Hands-on technical comfort with test automation, evaluation tooling, observability, or scripting, and the ability to partner with engineers to build pragmatic evaluation pipelines. Highly regarded: Experience with LLM evaluation pipelines, automated eval tooling, LLM judges, or agent observability platforms. Background in traditional QA or quality engineering, particularly in environments transitioning to AI-augmented workflows. Familiarity with safety, bias, and compliance evaluation for AI systems. Experience in SaaS or enterprise software environments, ideally with exposure to student housing, property management, or similar domainspecific AI products. Exposure to production monitoring of AI systems, including drift detection and post-release quality tracking. Reasons to join our Team: You will be part of a vibrant and supportive culture, with weekly team lunches. We are a flexible and hybrid workplace. You will have access to professional development opportunities. Be part of a global team, participate in monthly global meetings and partner with team members in different countries. Z-Factor: Our most celebrated value, you will work with a team of caring, energetic, high-performing, and passionate people who have fun supporting our vision, innovation & continuous improvement. We are proud of our diverse workforce and are dedicated to creating a safe and welcoming environment for all employees. People from various ethnicities, ages, genders, and abilities are encouraged to apply. Please note, StarRez requires all candidates to have full work rights in Australia. Notice to external Recruiters and Recruitment Agencies: StarRez will not accept unsolicited resumes from recruitment agencies, headhunters, or any other third parties for this role through this website or directly to any employee. StarRez and any of our subsidiaries will not pay fees to any third-party agency or company. In addition, we ask that you do not reach out to any employee with regards to this position, or any other positions, now, or in the future. StarRez is an equal opportunity employer.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.