Senior ML Engineer (Agentic Search)

Accountabilities:

You will lead the development and optimization of machine learning capabilities for large-scale, agent-oriented search systems. Your work will focus on improving search quality, retrieval performance, and operational efficiency while collaborating with engineering teams to bring advanced models into production.

• Design, train, deploy, and maintain machine learning models for retrieval, reranking, and search relevance in production environments.

• Build and optimize embedding-based indexing and large-scale information retrieval systems.

• Develop machine learning models supporting web crawling, data selection, and content understanding.

• Define meaningful quality metrics for agent-native search and build robust model evaluation pipelines.

• Work on large-scale systems handling high-throughput query workloads and continuous production traffic.

• Collaborate with engineering teams to integrate ML models into production services and infrastructure.

• Analyze trade-offs between latency, search quality, scalability, and operational cost.

• Research, experiment with, and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems.

• Contribute to product strategy, technical architecture, and engineering decisions.

• Continuously improve model performance, evaluation methods, and production reliability.

Requirements:

The ideal candidate brings strong applied machine learning or software engineering experience, a solid understanding of modern ML techniques, and practical expertise deploying models in production. You should be comfortable tackling complex retrieval and ranking problems, working with large-scale data systems, and collaborating with distributed teams in a fast-moving environment.

• At least 5 years of experience in software engineering or applied machine learning.

• Strong programming skills in Python, Go, or C++.

• Proven experience deploying and operating machine learning models in production systems.

• Hands-on experience with retrieval, ranking, recommendation systems, or related machine learning applications.

• Strong understanding of machine learning and modern deep learning techniques.

• Experience working with large-scale data systems and high-throughput environments.

• Ability to design evaluation frameworks and define meaningful model performance metrics.

• Strong analytical, problem-solving, and critical-thinking skills.

• A product-oriented mindset, with a focus on measurable impact, experimentation, and iterative improvement.

• Ability to take ownership of technical challenges and collaborate effectively with distributed teams.

Nice-to-have qualifications:

• Experience with search engines or large-scale information retrieval systems.

• Familiarity with embeddings, transformer architectures, and modern natural language processing techniques.

• Experience developing LLM-powered applications or agent-based systems.

• Contributions to open-source projects, technical publications, or conference presentations.

• Participation in competitive machine learning initiatives, such as Kaggle, or other evidence of strong technical expertise.

Benefits:

• Competitive compensation.

• Career development and continuous learning opportunities.

• Flexibility and meaningful ownership of technical work.

• Collaborative, innovative, and fast-paced engineering culture.

• Opportunity to contribute to impactful AI projects and advanced search technologies.

• International working environment with experienced engineering and research teams.

• Opportunity to influence technical architecture and help shape emerging AI infrastructure.