Machine Learning Engineer: Ranking & Personalization
Quick Summary
Competitive compensation: Annual base salary ranging from $80,000 to $120,000 USD, depending on skills, experience, qualifications, and interview results.
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,
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.
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Design, develop, deploy, and maintain scalable, real-time machine learning services that power ranking and personalization across e-commerce products, platforms, and industry verticals.
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Enhance personalized shopping experiences by developing and refining recommendation, ranking, and discovery capabilities based on user behavior and preferences.
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Analyze user interactions and behavioral data to build effective models that deliver more relevant and engaging customer experiences.
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Research, evaluate, and implement different personalization approaches to identify solutions that maximize customer value and business performance.
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Formulate and validate hypotheses through experimentation and A/B testing, measuring the impact of personalization improvements on key business metrics.
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Collaborate with engineering teams, including ranking specialists, as well as cross-functional technical and non-technical stakeholders to develop and continuously improve personalization features.
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Communicate findings, technical decisions, and business outcomes clearly to stakeholders, translating complex machine learning concepts into actionable insights.
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Take ownership of problems from investigation through implementation, continuously improving the reliability, scalability, and effectiveness of production systems.
Requirements
~1 min read-
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.
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Proficiency in Python, SQL, and big data technologies, with the ability to support end-to-end machine learning product development.
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Proven experience building, deploying, and maintaining production-grade personalization, recommendation, or ranking systems.
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Practical knowledge of designing and analyzing A/B tests and using experimental results to guide product and model improvements.
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Experience with a major public cloud platform is an advantage.
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Strong analytical and problem-solving skills, including the ability to investigate complex challenges, identify root causes, and translate findings into practical solutions.
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Excellent English communication skills, with the ability to explain technical concepts and demonstrate business value to diverse stakeholders.
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Experience working effectively in cross-functional teams, balancing technical priorities with product and business objectives.
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Ability to work autonomously, manage responsibilities independently, and take initiative in identifying opportunities for improvement.
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A proactive, collaborative mindset, combined with empathy, accountability, and a genuine interest in delivering impactful machine learning solutions.
What We Offer
~2 min readLocation & Eligibility
Listing Details
- Posted
- October 12, 2026
- First seen
- October 12, 2026
- Last seen
- October 12, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 80%
- Scored at
- October 12, 2026
Signal breakdown
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