[All remote jobs](https://jobicy.com/jobs.md)[![Nebius logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/d90c0566-221.webp)](https://jobicy.com/company/nebius.md)[Nebius](https://jobicy.com/company/nebius.md) # Senior ML Engineer (Agentic Search) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)ShareRemote from[Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished27 Oct 2026Apply before26 Nov 2026Listing views47Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Nebius is seeking a senior machine learning engineer to build production-grade agentic search systems for retrieval, reranking, indexing, and content understanding. The role combines applied deep learning with ML platform engineering, including training-data generation, evaluation, orchestration, GPU scheduling, and low-latency inference. The engineer will partner closely with software teams to optimize relevance, latency, quality, and cost for a 24x7 web-scale service. Strong Python plus Go or C++, production ML deployment experience, and expertise in transformers, embeddings, and rigorous model evaluation are required. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a technically demanding senior role spanning retrieval ML, LLM-integrated systems, distributed training infrastructure, and production serving at web-search scale. Success requires independent architectural judgment while balancing model quality, latency, reliability, and operating cost. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$225,000US market range (per year)$190k–$260k0$286k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The displayed figures are estimated annual USD market compensation for a senior ML engineer specializing in production retrieval and search systems; the yearly period is inferred from the senior full-time role and US market convention. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Information Retrieval](https://jobicy.com/jobs?search_keywords=Information%20Retrieval.md)[Search Ranking](https://jobicy.com/jobs?search_keywords=Search%20Ranking.md)[Transformers](https://jobicy.com/jobs?search_keywords=Transformers.md)[Embeddings](https://jobicy.com/jobs?search_keywords=Embeddings.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Go](https://jobicy.com/jobs?search_keywords=Go.md)[C++](https://jobicy.com/jobs?search_keywords=C%2B%2B.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[GPU Scheduling](https://jobicy.com/jobs?search_keywords=GPU%20Scheduling.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[ML Pipelines](https://jobicy.com/jobs?search_keywords=ML%20Pipelines.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Inference Optimization](https://jobicy.com/jobs?search_keywords=Inference%20Optimization.md)[Experiment Tracking](https://jobicy.com/jobs?search_keywords=Experiment%20Tracking.md) Sample interview questionsHow would you evaluate a new reranking model before deploying it to a production search system?I would begin with offline evaluation using representative, time-split query-document data and metrics such as NDCG, MRR, Recall@K, latency, and calibration. I would segment results by query type, freshness, language, and tail queries, then validate promising candidates through a guarded online A/B test with reliability and cost metrics. What trade-offs do you consider when serving embedding or cross-encoder models under strict latency requirements? I balance relevance gains against p95 and p99 latency, throughput, GPU utilization, and cost. Typical levers include model distillation, batching, quantization, compilation, caching, candidate-set sizing, and a multi-stage retrieval architecture that reserves expensive reranking for the most valuable candidates. Describe an ML pipeline you would build for continuous retraining of retrieval models. I would create versioned pipelines for data ingestion, labeling or weak-supervision generation, quality validation, training, offline evaluation, model registration, and controlled deployment. The pipeline should track data and model lineage, detect distribution shifts, enforce metric gates, and support rollback when online quality or system health degrades. How would you define meaningful metrics for an agentic search product? I would combine retrieval metrics with task-level measures, such as answer grounding, source coverage, factuality, freshness, successful task completion, and user follow-up rate. Operational metrics including latency, availability, crawl-to-index delay, and cost per successful task are also essential because model quality alone is insufficient in production. How do you collaborate with platform and product engineers when an ML improvement increases infrastructure cost? I would quantify the incremental quality improvement alongside GPU, storage, latency, and operational costs, then propose alternatives such as selective routing, smaller models, caching, or staged reranking. I aim to make the decision transparent through experiments and dashboards so the team can choose the best product-level trade-off. Opportunity details ## About this role. About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. We are seeking a Senior ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. Our focus is agentic search, where AI systems plan, retrieve and refine rather than just return results. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24×7, operating at the scale of web search. Your responsibilities: * Train and deploy ML models for retrieval, reranking, and search relevance, such as embedding models and cross-encoders, in production * Develop models supporting crawling, data selection, and content understanding * Build and maintain the data, training and evaluation pipelines behind our models: training-data generation, evaluation sets and retraining * Contribute to and improve our ML platform: training and orchestration workflows (Kubernetes, GPU scheduling, experiment tracking, model lifecycle) and inference optimisation for low-latency serving * Work closely with engineering to integrate and deploy ML models in production services, balancing latency, quality and cost * Apply the latest architectural advances and training procedures for retrieval and LLM-integrated systems Must-haves: * 5+ years of experience in software engineering or applied machine learning * Strong programming skills in Python and at least one of Go or C++ * Proven experience deploying ML models in production systems * Solid understanding of machine learning and modern deep learning, including transformers and embeddings * Experience building data or ML pipelines and working with workflow orchestration * Ability to define meaningful model metrics and evaluate models rigorously * Product-oriented mindset with a focus on impact and iteration Nice-to-haves: * Hands-on experience with retrieval, ranking, or recommendation * Experience with Kubernetes, distributed training or GPU scheduling, or inference optimisation (batching, quantisation, compilation) * Contributions to open-source projects, technical publications, or conference talks * Strong results on public benchmarks or leaderboards (e.g. MTEB, BEIR) or models released on Hugging Face Benefits & Perks: * Competitive compensation * Career growth and learning opportunities * Flexibility and ownership * Collaborative and innovative culture * Opportunity to work on impactful AI projects * International environment and talented teams What’s it like to work at Nebius: Fast moving – Bold thinking – Constant growth – Meaningful impact – Trust and real ownership – Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Show more [Apply now >](https://jobicy.com/jobs/157433-senior-ml-engineer-agentic-search.md) > Annual salary information is not provided for this position. 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