Machine Learning Engineer – Search & Retrieval Systems

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineer – Search & Retrieval Systems in the United States.

This role sits at the core of a high-scale AI-powered product, responsible for building and evolving the search and retrieval systems that power intelligent product discovery. You will design how millions of items are surfaced, ranked, and personalized in response to dynamic user intent, ensuring results are fast, relevant, and context-aware. Operating in a production-driven environment, you will work at the intersection of information retrieval, machine learning, and applied NLP to continuously improve ranking quality and system intelligence. The role spans full ownership of the search stack—from retrieval and vector search to ranking models and business logic shaping final results. You will build adaptive systems that learn from real user behavior and evolve over time, directly impacting user experience and conversion quality. This is a highly iterative, experimental environment where strong engineering and ML rigor are combined with product-focused thinking.

Accountabilities:

In this role, you will own the end-to-end search and retrieval systems, ensuring continuous improvement in relevance, ranking quality, and system adaptability. You will work across ML modeling, search infrastructure, and data-driven experimentation to improve product discovery performance at scale.

Own and evolve hybrid search systems, including lexical search, vector retrieval, and multi-stage ranking pipelines
Design and deploy learning-to-rank models (e.g., LightGBM, XGBoost, LambdaMART) for production search ranking
Build adaptive retrieval systems that dynamically adjust search behavior based on query intent, context, and category
Develop and maintain Elasticsearch-based infrastructure, including index design, query optimization, and hybrid retrieval strategies
Build feedback loops using behavioral signals (CTR, conversions, engagement) to improve ranking and retrieval quality
Design offline and online evaluation frameworks, including A/B testing and ranking metric analysis (nDCG, MRR, precision/recall)
Own product enrichment pipelines, including LLM-based metadata generation and large-scale indexing workflows
Integrate query understanding outputs (intent, attributes, constraints) into retrieval and ranking logic
Build scalable business logic layers for product ordering, including separation of organic and sponsored results
Instrument and monitor search performance, ensuring regression detection and continuous system improvement

Requirements:

The ideal candidate has strong experience in search systems, applied machine learning, and production-scale retrieval engineering. You are pragmatic, highly analytical, and comfortable building systems that combine ML models with real-time search infrastructure.

5–8+ years of experience in search, ranking, or retrieval systems in production environments
Strong expertise in Elasticsearch or similar technologies (OpenSearch, Solr, Vespa), including indexing and query design
Hands-on experience with learning-to-rank approaches such as LambdaMART, LightGBM, or XGBoost
Strong Python engineering skills with production-grade software development practices
Experience with dense retrieval, embeddings, and vector search (ANN systems, semantic retrieval)
Solid understanding of ML evaluation methodologies (nDCG, MRR, A/B testing, offline/online metrics)
Experience building end-to-end ML pipelines from training data to deployment and monitoring
Ability to work with behavioral data and translate signals into ranking improvements
Strong problem-solving skills with a focus on measurable product impact
Excellent communication skills and ability to collaborate across engineering and product teams

Benefits:

Competitive compensation ($225,000 – $280,000 USD depending on experience and location)
Equity in the form of stock options
Comprehensive medical, dental, and vision insurance
401(k) retirement plan
Fully remote work across the United States
Flexible PTO and company holidays
Periodic offsites and team gatherings
Opportunity to work on cutting-edge AI search and retrieval systems at large scale

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.

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Machine Learning Engineer – Search & Retrieval Systems

Requirements:

Benefits:

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