Senior Software Engineer - Data Search (remote, Europe)

Modash is a company founded in 2018 that provides a suite of tools helping brands scale partnerships with online content creators. More than 2,700 companies, including Stanley 1913, Sennheiser, and NordVPN, use Modash to manage and scale their influencer marketing work. The company operates with a fast, async-first culture, is remote-first with almost 100 people distributed across 20+ countries, and has an 8-figure ARR across two products with a $12M Series A investment.

• Demonstrated experience building large-scale data or backend products with considerations for volume, latency, reliability, and cost.
• Experience shipping products from concept through to production, including scoping, architecture, implementation, release, measurement, and iteration.
• Ability to design distributed systems, understanding throughput, failure modes, data flow, scalability, and operational tradeoffs.
• Experience working with LLM-powered or agentic features in production, understanding model tradeoffs between capability, latency, and cost.
• Ability to work autonomously on ambiguous problems, gathering requirements and making progress with incomplete context.
• Strong communication skills to collaborate across teams and explain technical tradeoffs to technical and non-technical stakeholders.
• Comfortable working in a fast-moving product environment with rapid learning and iterative shipping.

Bonus experience:

• Familiarity with multimodal embeddings, vector databases, semantic search, ranking algorithms, model deployment, self-hosted models, or GPU infrastructure.
• Interest in the creator economy.

• Improve creator search relevance, retrieval, filtering, ranking, and speed across massive datasets.
• Build systems that generate and utilize embeddings from images, video, text, and audio at large scale.
• Evaluate machine learning models and technologies to pragmatically ship new search features.
• Own the full development lifecycle: problem definition, requirements gathering, architecture design, implementation, release, outcome measurement, and continuous improvement.

Typical week activities:

• Diagnose and improve search result relevance issues.
• Develop pipelines for generating multimodal embeddings and test retrieval quality.
• Collaborate with Data Insights to define and expose new datapoints for search.
• Test reranking models for relevance and production readiness.
• Review production metrics, investigate regressions, and share findings with the team.