Senior Software Engineer - Data Search

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer – Data Search based in Switzerland. This is a senior engineering role focused on building and improving a large-scale creator search experience used by thousands of businesses worldwide. You’ll work across the full search stack, from data and indexing pipelines to retrieval, embeddings, ranking, relevance, and low-latency serving. The role combines distributed systems, multimodal data, vector search, and LLM-powered technologies to solve complex search problems at significant scale. You’ll work with hundreds of millions of profiles and billions of media files, turning messy, high-volume data into useful intelligence. You’ll have substantial autonomy and ownership, taking ambiguous product challenges from discovery through architecture, implementation, production, and measurement. You’ll collaborate closely with data, product, engineering, customers, and leadership teams in a fast-moving, async-first environment.

Improve creator discovery across hundreds of millions of profiles and billions of media files by enhancing retrieval, filtering, ranking, relevance, performance, and the overall search experience. Design and build systems that generate and leverage multimodal embeddings from images, video, text, and audio at large scale. Develop new search capabilities and take promising ideas from experimentation to reliable production systems, balancing search quality, latency, scalability, and cost. Own technical problems end to end, including understanding customer needs, gathering requirements, defining solutions, designing architecture, writing code, launching features, measuring outcomes, and iterating based on evidence. Diagnose and resolve relevance and performance issues across data, query logic, retrieval, ranking, models, and product decisions. Evaluate new models and technologies pragmatically, understanding their tradeoffs and identifying where they can create meaningful improvements to the search experience. Work closely with data and product teams to introduce new datasets, datapoints, and search capabilities while ensuring appropriate coverage and data quality. Contribute to technical direction and system architecture while maintaining reliable, scalable, and maintainable production services. Share findings, technical insights, and learnings with colleagues and contribute to a culture of strong engineering practices and continuous improvement. Requirements: Proven experience building large-scale data, backend, or search-oriented products where volume, latency, reliability, scalability, and cost are important considerations. Demonstrated ability to take products or substantial technical initiatives from concept through architecture, implementation, production release, measurement, and iteration. Strong experience designing and working with distributed systems, including understanding throughput, data flows, scalability, failure modes, reliability, and operational tradeoffs. Professional experience building LLM-powered or agentic features in production, with a practical understanding of model capabilities, limitations, latency, and cost. Strong problem-solving skills and the ability to work autonomously on ambiguous problems, gather missing context, ask effective questions, and turn uncertainty into action. Excellent communication and collaboration skills, with the ability to explain complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders. Experience working in fast-moving product environments where teams ship incrementally, learn from data, and adapt quickly to changing priorities. Strong programming skills and comfort working with technologies such as Python, TypeScript, or Node.js. Experience with cloud infrastructure and modern data platforms is highly valuable, particularly AWS or GCP. Experience with technologies such as Elasticsearch, vector databases, distributed data processing, data orchestration, or infrastructure as code is advantageous. Bonus experience includes multimodal embeddings, semantic search, ranking algorithms, model deployment, self-hosted models, GPU infrastructure, or vector search technologies. Curiosity about creator platforms, social media, or the creator economy is welcome, although prior industry experience is not required. Benefits: Competitive compensation: Annual salary range of €100,000–€130,000 , plus stock options. The exact package depends on location, employment type, skills, and experience. Meaningful equity: A significant stock option package designed to provide employees with meaningful ownership as the organization grows. Fully remote in Europe: Work remotely from wherever you do your best work, with some working-hour overlap around GMT+3. Flexible working hours: Focus is placed on outcomes and impact rather than fixed login times. Unlimited paid vacation: Take the time you need to rest and recharge. Personal development support: Access funding for courses, books, conferences, and other opportunities that support your professional growth. Real technical ownership: Take challenging search and engineering problems from initial idea through production without unnecessary layers of process. Regular company offsites: Connect with colleagues in person through recurring offsites while maintaining a remote-first working model. Deep-work culture: Purposeful meetings, focused collaboration, and protected time for designing, building, optimizing, and launching. Fast-moving environment: Work alongside experienced engineers and specialists while having the autonomy to make meaningful technical decisions. Modern technology stack: Work with technologies including AWS, GCP, Pulumi, Python, TypeScript, Node.js, PySpark, Airflow, Milvus/Zilliz, Elasticsearch, Apache Iceberg, SageMaker, DynamoDB, S3, Glue, Kinesis, Lambda, ECS, and Aurora. Efficient hiring process: The interview process is designed to move quickly and can be completed in under a week, typically including an introductory conversation, coding interview, system design interview, team interview, and a final culture and alignment conversation.

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