Amazon Science
Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services
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Applied Scientist, Intelligent Talent Acquisition - Lead Generation & Detection Services
Job Opportunity: Applied Scientist (Recommender Systems) - Intelligent Talent Acquisition, Amazon
Want to make a real difference to people’s lives? Join Amazon’s Recommendations team in Intelligent Talent Acquisition (ITA), where you’ll design and build fair, scalable, and explainable machine learning systems that automate global recruitment—and help Amazon scale hiring for diverse, exceptional teams.
About the Role
You’ll develop cutting-edge machine learning products powering Amazon Jobs and internal hiring platforms, using techniques like representation learning, reinforcement learning, and probabilistic modeling to optimise talent discovery at scale.
Your high-impact work will:
- Transform how job seekers find roles and recruiters identify top candidates
- Improve efficiency for recruiters while ensuring a diverse hire pipeline
- Drive innovation through collaboration with scientists across Amazon
- Create systems that make hiring quicker, more fair, and more predictable
This position offers real impact: scaling fair hiring while pushing the boundaries of ML in high-stakes talent acquisition—not just for technology, but across global industries and student placements.
Key Responsibilities
- Build and deploy machine learning models at global scale, focused on fair, explainable, and high-performance recommender systems for job seekers and recruiters (Full lifecycle: design, testing, deployment, monitoring, refinement)
- Collaborate closely with engineers, scientists, and product managers to define requirements, test hypotheses, and deliver business-critical hiring solutions
- Solve complex ML problems leveraging optimal algorithms/data structures, balancing effectiveness with maintainability
- Publish novel ML approaches, sharing insights to help the entire Intelligent Talent Acquisition team innovate further—including influencing scientific research company-wide
- Design experimental frameworks to measure model performance, fairness, and business impact
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A Day in the Life
Your work blends software engineering, ML research, and real-world collaboration:
- Morning: Code reviews for trustworthy ML, tactically refining recommendation systems (e.g. job-candidate matching pipelines for A/B testing)
- Afternoon: Analysing A/B test results, iterating on sequential ranking models and diversity metrics to enhance fairness sixes in recruitment
- Collaborative breaks: Scientific discussions with peers, shaping next-generation hiring AI
About the Team
An interdisciplinary team of software engineers and applied scientists based in Edinburgh.
Our mission: Match the right people to the right roles—transforming the candidate experience and recruiter efficiency for Amazon’s global hiring strategy.
Work culture:
- Collaborative & mentoring-driven with daily knowledge sharing
- Tight integration with product teams to align on real hiring challenges
- Impact at scale: Improve hire diversity, compliance, and recommendation precision across student and industry jobs worldwide
Amazon’s team thrives on innovation, application over theory, and impact—not just patents or papers. Here, you’ll push real-world ML limits to solve tough problems with engineering rigor.
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Qualifications
Basic Qualifications
- Experience building business-critical ML models around recommendation, text, or structured data
- Proficiency in enough Python/Java/C++/Scala to build and deploy scalable solutions at scale
- Impeccable English communication skills (written, spoken, and reading); all team chats in English
- Strong analytical thinking, scientific rigor, and attention to detail
Preferred Qualifications (If any!)
- PhD in Computer Science/ML/Engineering/AI-related fields
- Research publications or patents (especially in recommender systems, fairness, or reinforcement learning)
- Stakeholder collaboration: Experience defining and validating ML requirements across cross-functional teams
Amazon Commitment to Inclusivity
- An equal opportunity employer. Recruitment decisions profit from experience/specific abilities only.
- We value diversity—your perspective, path, and work ethic help shape a more collaborative, inclusive hire pipeline for Amazon.
- Accommodations available: If you require help completing the application process, visit Amazon’s access portal.
Are you a team player who turns hypotheses into scalable impact? Apply now!
(Note: Many candidates inquire where this role is based—it’s currently open to hires in Edinburgh and select remote locations.)
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