Applied AI Engineer – Systems & Reliability

Job not on LinkedIn

🕒 July 27

🌏 Anywhere in the World

⏰ Full Time

🟡 Mid-level

🟠 Senior

⚙️ Systems Engineer

👻 Ghost score 24%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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HiPeople

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

👥 HR Tech

☁️ SaaS

🤖 Artificial Intelligence

💰 $2.7M Seed Round - HiPeople on 2022-10

HR Tech • SaaS • Artificial Intelligence

HiPeople is an AI-powered hiring platform that helps companies automate and augment recruiting workflows. It offers AI screening to process inbound applications in real time, skills assessments for bias-reduced, skills-based hiring, 24/7 interview capabilities in many languages, and AI-driven, fraud-protected reference checks. The platform integrates deeply with applicant tracking systems and emphasizes privacy and compliance to deploy AI throughout the hiring funnel, reducing recruiter time and accelerating hiring decisions.

📋 Description

• Own evaluation systems and quality standards • Build and maintain evaluation pipelines for core AI workflows across screening, interviews, assessments, and references • Define metrics, benchmarks, and acceptance criteria for AI outputs • Track performance over time (quality trends, drift, regressions) and make results visible across the team • Drive continuous improvement of AI performance • Identify issues across prompts, workflows, and data pipelines using both quantitative analysis and deep dives into real cases • Design and implement improvements across: • prompting strategies • model selection, configuration, and fine-tuning • input data quality and preprocessing • orchestration and workflow design • Push new systems from “working” (80%) to reliable and high-quality (95%+) • Ensure reliability, monitoring, and stability • Build and improve monitoring for AI systems (e.g. dashboards, alerts, tracing) • Detect and prevent failure modes, breakdown risks, and performance degradation • Monitor usage, rate limits, and capacity to ensure stable operation at scale • Drive testing, CI, and safe shipping practices • Integrate AI and prompt testing into CI (e.g. regression tests, golden datasets, staging environments) • Define standards and tooling so product and engineering teams can safely ship without introducing regressions • Act as a quality gate for AI-related changes • Own AI system audits and compliance support • Prepare and support internal and external audits (e.g. SOC 2 and beyond) • Provide evidence, documentation, and artifacts for AI system behavior and controls • Translate audit findings into concrete improvements in systems and processes • Productionize AI workflows (not just prototype them) • Build and productionize AI workflows that meet defined quality and reliability standards • Support product and engineering teams in integrating AI cleanly into product logic and user experience • Ensure new AI capabilities are robust, measurable, and maintainable before release

🎯 Requirements

• 100% alignment with our Ops Principles (if you feel this isn’t you, do not apply) • Excitement for building in Go • Experience working with AI/ML systems, LLMs, or data-intensive applications • High ownership mindset and attention to detail • Strong interest in quality, reliability, and system performance, not just building features • Ability to debug complex systems across prompts, models, and data pipelines • Clear communication and documentation skills • Comfort improving systems and processes, not just using them • Experience with evaluation methods, metrics, or experimentation is a strong plus • Familiarity with monitoring, CI/CD, and production systems is a plus

🏖️ Benefits

• Direct ownership of one of the most critical parts of the company: AI quality and reliability • Work closely with founders on core product and technical decisions • Competitive salary and meaningful stock options • Educational stipend to support ongoing learning and development • The best team to work with (true story!)