Machine Learning Engineer: Ranking & Personalization

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Machine Learning Engineer: Ranking & Personalization based in Romania.
Join a collaborative, AI-driven environment where machine learning powers smarter e-commerce search, discovery, and shopping experiences. In this role, you will design, build, and maintain high-performance, real-time personalization services that reach hundreds of millions of users worldwide. You will work closely with engineers, data specialists, and business stakeholders to develop innovative solutions that improve product discovery and customer engagement. By leveraging user behavior, experimentation, and advanced machine learning techniques, you will help deliver more relevant shopping experiences. Your work will directly contribute to measurable business outcomes, including improved conversion rates and revenue growth. This is an opportunity to combine technical expertise, analytical thinking, and a strong sense of ownership while making a meaningful impact in a supportive, remote-first team.
Accountabilities
Design, develop, deploy, and maintain scalable, real-time machine learning services that power ranking and personalization across e-commerce products, platforms, and industry verticals.
Enhance personalized shopping experiences by developing and refining recommendation, ranking, and discovery capabilities based on user behavior and preferences.
Analyze user interactions and behavioral data to build effective models that deliver more relevant and engaging customer experiences.
Research, evaluate, and implement different personalization approaches to identify solutions that maximize customer value and business performance.
Formulate and validate hypotheses through experimentation and A/B testing, measuring the impact of personalization improvements on key business metrics.
Collaborate with engineering teams, including ranking specialists, as well as cross-functional technical and non-technical stakeholders to develop and continuously improve personalization features.
Communicate findings, technical decisions, and business outcomes clearly to stakeholders, translating complex machine learning concepts into actionable insights.
Take ownership of problems from investigation through implementation, continuously improving the reliability, scalability, and effectiveness of production systems.
Requirements
At least 3 years of professional experience in applied machine learning, with a strong understanding of how to develop and deploy ML solutions in real-world environments.
Proficiency in Python, SQL, and big data technologies, with the ability to support end-to-end machine learning product development.
Proven experience building, deploying, and maintaining production-grade personalization, recommendation, or ranking systems.
Practical knowledge of designing and analyzing A/B tests and using experimental results to guide product and model improvements.
Experience with a major public cloud platform is an advantage.
Strong analytical and problem-solving skills, including the ability to investigate complex challenges, identify root causes, and translate findings into practical solutions.
Excellent English communication skills, with the ability to explain technical concepts and demonstrate business value to diverse stakeholders.
Experience working effectively in cross-functional teams, balancing technical priorities with product and business objectives.
Ability to work autonomously, manage responsibilities independently, and take initiative in identifying opportunities for improvement.
A proactive, collaborative mindset, combined with empathy, accountability, and a genuine interest in delivering impactful machine learning solutions.
Benefits
Competitive compensation: Annual base salary ranging from $80,000 to $120,000 USD, depending on skills, experience, qualifications, and interview results.
Stock options: Equity opportunities in addition to the base salary.
Fully remote work: Work remotely and choose where you live.
Flexible time off: Unlimited vacation, with employees strongly encouraged to take at least three weeks off annually.
Home office support: A work-from-home stipend to help you create a comfortable and productive workspace.
Equipment provided: Apple laptops supplied to new employees.
Learning and development: An annual training and development budget to support professional growth.
Family support: Maternity and paternity leave for eligible employees.
Team connection: Regular team offsites that encourage collaboration, relationship-building, and shared learning.
Supportive culture: Work alongside talented, empathetic colleagues in an open and cooperative environment that values ownership, inclusion, and meaningful impact.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Work arrangement
Yes