Senior Software Engineer - Data Search

pbThis 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. /b /p pThis 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. /p h3Accountabilities /h3 ul liImprove 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. /li liDesign and build systems that generate and leverage multimodal embeddings from images, video, text, and audio at large scale. /li liDevelop new search capabilities and take promising ideas from experimentation to reliable production systems, balancing search quality, latency, scalability, and cost. /li liOwn 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. /li liDiagnose and resolve relevance and performance issues across data, query logic, retrieval, ranking, models, and product decisions. /li liEvaluate new models and technologies pragmatically, understanding their tradeoffs and identifying where they can create meaningful improvements to the search experience. /li liWork closely with data and product teams to introduce new datasets, datapoints, and search capabilities while ensuring appropriate coverage and data quality. /li liContribute to technical direction and system architecture while maintaining reliable, scalable, and maintainable production services. /li liShare findings, technical insights, and learnings with colleagues and contribute to a culture of strong engineering practices and continuous improvement. /li /ul h3Requirements /h3 ul liProven experience building large-scale data, backend, or search-oriented products where volume, latency, reliability, scalability, and cost are important considerations. /li liDemonstrated ability to take products or substantial technical initiatives from concept through architecture, implementation, production release, measurement, and iteration. /li liStrong experience designing and working with distributed systems, including understanding throughput, data flows, scalability, failure modes, reliability, and operational tradeoffs. /li liProfessional experience building LLM-powered or agentic features in production, with a practical understanding of model capabilities, limitations, latency, and cost. /li liStrong problem-solving skills and the ability to work autonomously on ambiguous problems, gather missing context, ask effective questions, and turn uncertainty into action. /li liExcellent communication and collaboration skills, with the ability to explain complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders. /li liExperience working in fast-moving product environments where teams ship incrementally, learn from data, and adapt quickly to changing priorities. /li liStrong programming skills and comfort working with technologies such as Python, TypeScript, or Node.js. /li liExperience with cloud infrastructure and modern data platforms is highly valuable, particularly AWS or GCP. /li liExperience with technologies such as Elasticsearch, vector databases, distributed data processing, data orchestration, or infrastructure as code is advantageous. /li liBonus experience includes multimodal embeddings, semantic search, ranking algorithms, model deployment, self-hosted models, GPU infrastructure, or vector search technologies. /li liCuriosity about creator platforms, social media, or the creator economy is welcome, although prior industry experience is not required. /li /ul h3Benefits /h3 ul libCompetitive compensation: /b Annual salary range of b€100,000–€130,000 /b, plus stock options. The exact package depends on location, employment type, skills, and experience. /li libMeaningful equity: /b A significant stock option package designed to provide employees with meaningful ownership as the organization grows. /li libFully remote in Europe: /b Work remotely from wherever you do your best work, with some working-hour overlap around GMT+3. /li libFlexible working hours: /b Focus is placed on outcomes and impact rather than fixed login times. /li libUnlimited paid vacation: /b Take the time you need to rest and recharge. /li libPersonal development support: /b Access funding for courses, books, conferences, and other opportunities that support your professional growth. /li libReal technical ownership: /b Take challenging search and engineering problems from initial idea through production without unnecessary layers of process. /li libRegular company offsites: /b Connect with colleagues in person through recurring offsites while maintaining a remote-first working model. /li libDeep-work culture: /b Purposeful meetings, focused collaboration, and protected time for designing, building, optimizing, and launching. /li libFast-moving environment: /b Work alongside experienced engineers and specialists while having the autonomy to make meaningful technical decisions. /li libModern technology stack: /b 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. /li libEfficient hiring process: /b 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. /li /ul #J-18808-Ljbffr