About the company:
Our client is an international AI infrastructure company building a full-stack cloud platform for the global AI economy. The platform helps developers and enterprise teams work with data, model training and production deployment without the cost and complexity of building large in-house AI/ML infrastructure.
The company is built by engineers, for engineers. Its teams work on complex infrastructure problems across compute, storage, networking, GPU orchestration, inference optimization and applied AI.
The company operates globally, with R&D hubs across Europe, the UK, North America and Israel, and brings together 1,500+ specialists, including hundreds of engineers with deep expertise in hardware, software and AI R&D.
The team is now building an agent-native search platform for AI systems - a new web access layer designed specifically for AI agents and LLM-powered products.
The company is built by engineers, for engineers. Its teams work on complex infrastructure problems across compute, storage, networking, GPU orchestration, inference optimization and applied AI.
The company operates globally, with R&D hubs across Europe, the UK, North America and Israel, and brings together 1,500+ specialists, including hundreds of engineers with deep expertise in hardware, software and AI R&D.
The team is now building an agent-native search platform for AI systems - a new web access layer designed specifically for AI agents and LLM-powered products.
About the role:
We're looking for a Senior Applied ML Engineer who will work on machine learning systems powering retrieval, ranking, indexing and search relevance at scale.
This is a role for someone who wants to work not just with models, but with a complex production system where ML directly affects product quality, latency, cost and user experience.
The work spans different stages of the search pipeline: from helping decide which pages should be crawled and how content should be evaluated, to ranking large volumes of documents and selecting the most relevant results for AI systems.
The role requires strong applied ML expertise, production thinking and the ability to work with ambiguous problems where the best solution is not always predefined.
This is a role for someone who wants to work not just with models, but with a complex production system where ML directly affects product quality, latency, cost and user experience.
The work spans different stages of the search pipeline: from helping decide which pages should be crawled and how content should be evaluated, to ranking large volumes of documents and selecting the most relevant results for AI systems.
The role requires strong applied ML expertise, production thinking and the ability to work with ambiguous problems where the best solution is not always predefined.
What you will do:
- In this role, you’ll work on machine learning systems that power retrieval, reranking and search relevance in production.
- Your focus will include embedding-based indexing and large-scale retrieval systems: improving how documents are represented, searched and ranked at scale.
- You’ll also develop models that support crawling, data selection and content understanding, including systems that help identify which URLs or pages are worth processing and how often they should be revisited.
- Another important part of the role is defining and improving quality metrics for agent-native search, as well as building evaluation pipelines to measure relevance, freshness, reliability and overall search quality.
- The work involves high-throughput query workloads and large-scale production systems where performance, latency and cost trade-offs matter.
- You’ll collaborate closely with engineering teams to integrate ML models into production services and make sure they work reliably at scale.
- There will also be room to experiment with modern approaches in search, retrieval, recommendation systems, NLP, transformers and LLM-integrated systems.
- As the product evolves, you’ll contribute to product and architectural decisions in a fast-moving environment where many technical and product questions are still being actively explored.
Requirements:
- We're looking for candidates with 5+ years of experience in software engineering or applied machine learning.
- You should have strong programming skills in Python, Go or C++ and proven experience deploying ML models in production systems.
- Hands-on experience with retrieval, ranking, recommendation, personalization, search relevance, ads ranking, marketplace ranking or similar ML problems is important for this role.
- You should have a strong understanding of machine learning and modern deep learning techniques, as well as experience working with large-scale data systems or high-throughput environments.
- The role also requires the ability to design evaluation frameworks, define meaningful model metrics and reason about trade-offs between latency, quality and cost.
- A product-oriented mindset is important: the team is looking for someone who can connect ML work to real product impact, iterate quickly and take ownership of complex problems.
- You should also be comfortable working in a distributed international team and communicating closely with engineering and product stakeholders.
Nice to have:
- Experience with search systems or large-scale information retrieval will be a strong advantage.
- Familiarity with embeddings, transformers and modern NLP systems will also be useful.
- Experience working on LLM-powered products, AI agents or agent-based systems is a plus.
- Open-source contributions, technical publications, conference talks or competitive ML experience, such as Kaggle, can also be strong additional signals.
Format and benefits:
- The company is currently considering candidates based in Europe and the UK.
- Office-based: Amsterdam, Tel Aviv, London. Relocation assistance is available for senior candidates.
- European Union. Remote work is acceptable for strong candidates.
- The role offers competitive compensation, flexibility and work-life balance, as well as the opportunity to work in an international environment with a highly senior engineering team.
- You’ll join a collaborative and fast-moving culture where initiative, ownership and technical depth are valued.
- This is an opportunity to work on impactful AI infrastructure products and contribute to systems that may shape how AI agents access and use information from the web.
Send your CV on Telegram: @karinakava