Senior Software Engineer - Data Search (remote, Europe)

What you've done before • Built large-scale data or backend products. You have solid experience working with systems where volume, latency, reliability, and cost all matter. • Shipped products from concept to production. You’ve owned scoping, architecture, implementation, release, measurement, and iteration—not just one layer of the solution. • Designed distributed systems. You can reason clearly about throughput, failure modes, data flow, scalability, and operational tradeoffs. • Built LLM-powered or agentic features in production. You understand the practical differences between models and can balance capability against latency and cost. • Worked autonomously on ambiguous problems. You know how to gather requirements, ask useful questions, and turn incomplete context into forward motion. • Communicated clearly across teams. You can explain technical tradeoffs to engineers and non-engineers, give direct feedback, and collaborate without creating unnecessary process. • Worked in a fast-moving product environment. You’re comfortable learning quickly, shipping incrementally, and changing direction when the evidence does. Bonus points if you’ve worked with multimodal embeddings, vector databases, semantic search, ranking algorithms, model deployment, self-hosted models, or GPU infrastructure. Curiosity about the creator economy helps too, but we’ll get you up to speed. Our stack • AWS and GCP, with Pulumi for infrastructure as code • Python, TypeScript, and Node.js • PySpark on AWS EMR • Airflow • Milvus Vector DB through Zilliz • Elasticsearch • LLM batch APIs • Apache Iceberg • SageMaker, DynamoDB, S3, Glue, Kinesis, Lambda, ECS, and Aurora • Slack, GitHub, Linear, Notion, and Cursor The interview process We move quickly and can complete the process in under a week: • Intro chat • Coding interview • System design interview • Team interview • Culture and alignment conversation with our CEO, Avery Schrader

What we offer • Fully remote in Europe 🏠 Work from wherever you do your best work, with some overlap with GMT+3. • Compensation. Your compensation is made up of salary and stock options. As we're growing fast, the stock option package is especially significant. Annual salary range is 100,000€ - 130,000€ for this role. We hire across Europe, so the exact number depends on your location, employment type, skills, and experience. • Flexible hours ⏱ We care about outcomes, not when you log on. • Unlimited paid vacation 🌴 If you’re rested and happy, you do better work. Take what you need. • Personal development support 🧠 Courses, books, and conferences are on us when they help you grow. • Real ownership 💡 Take difficult search problems from idea to production without layers of unnecessary process. • Regular offsites ✈️ We’re remote-first, but we make time to connect, collaborate, and have fun together in person. And a little more about us... Founded in 2018 by a high-school dropout and a Canadian (yes, we’re also shocked it’s going so well), Modash is building a suite of tools that help brands scale partnerships with online content creators. 2,700+ companies like Stanley 1913, Sennheiser, and NordVPN already use Modash to manage and scale their influencer marketing work. And we're just getting started. Over the coming decade, brand investment in creators will continue to boom, and Modash will be at the centre of it all. Modash is here to stay. We have 8-figures in ARR across two products, a $12M series A investment, and we are default alive. We are building a company that will still be here in 20 years; not rushing towards an exit. We’re almost 100 people distributed across 20+ countries, operating with a fast, async-first culture. If you join Modash, you’ll be surrounded by people who truly want to be the greatest at their craft. People who make you better. Interesting people too, who have done everything from building solar cars, to hanging out with Metallica and Bon Jovi. Come join us. Be great, do great things, create great memories, all while making a great impact. Do it.

Remote — Data Search Team — Full-time Hey, I'm Adrian. I’m hiring a Senior Software Engineer to join the Data Search team at Modash. Modash helps brands find, understand, and work with creators across Instagram, TikTok, and YouTube. More than 2,700 companies—including Stanley 1913, Sennheiser, and NordVPN—use us to manage and scale their creator partnerships. Behind that product is a fascinating search problem: helping customers find the right people across 400M+ creator profiles and billions of media files. We’re combining large-scale data processing, traditional retrieval, vector search, multimodal embeddings, and LLMs to make that possible. That’s where you come in. Why we're hiring Search at Modash isn't an internal platform or a support function. It is one of the core products customers use to discover creators. The scale is large, the data is messy, and the search intent is often complex. A customer might be looking for creators in a specific niche, people whose content conveys a certain visual style, or accounts that resemble a group they already know. Solving that well requires more than adding another filter or calling an LLM API. We need a senior engineer who can work across the full retrieval system—from data and indexing pipelines to embeddings, ranking, relevance, and low-latency serving—and take ambiguous product problems all the way to production. You’ll join the specialised Data Search team and work closely with Data Core, Data Insights, product teams, customers, and company leadership. You’ll have real autonomy, but you won’t work in isolation. If you want a feel for how we think about building software, check our Engineering Blog. What you'll actually own 1. Make creator search meaningfully better. You’ll improve how customers discover creators across 400M+ profiles and billions of media files. That includes retrieval, filtering, ranking, relevance, speed, and the product decisions that connect them. 2. Turn multimodal data into searchable intelligence. You’ll build systems that generate and use embeddings from images, video, text, and audio at massive scale—then make those signals useful in a real customer-facing search experience. 3. Ship new search capabilities into production. You’ll evaluate models and technologies pragmatically, understand tradeoffs around cost, latency, and quality, and move promising approaches from experiment to a reliable production system within weeks rather than quarters. 4. Own the system end to end. You’ll help shape the problem, gather requirements, design the architecture, write the code, release it, measure the outcome, and improve it. Senior engineers here own results, not just implementation tasks. What the day-to-day looks like Here’s what a typical week might include: • Monday. A customer search is returning technically relevant but unhelpful results. You inspect the retrieval and ranking stages, identify where intent is being lost, and propose a measurable improvement. • Tuesday. Deep-focus time. You build a pipeline to generate multimodal embeddings across a large batch of creator content and test how the new representation affects retrieval quality and cost. • Wednesday. You work with Data Insights on a new in-house datapoint. Together, you agree on its definition, coverage, and data-quality requirements, then expose it in Search to give customers more ways to discover creators. • Thursday. You test a reranking model on a fixed set of real customer queries. You measure how much it improves relevance against the latency and inference cost it adds, then decide what's ready for production. • Friday. You review production metrics, investigate a relevance regression, and share what you learned with the team. The fix may be in the model, the data, the query logic, or the product itself—you follow the evidence. We keep meetings purposeful and protect time for deep work. You’ll have a short standup, close collaboration when it helps, and plenty of space to design, build, optimise, and launch. What you've done before • Built large-scale data or backend products. You have solid experience working with systems where volume, latency, reliability, and cost all matter. • Shipped products from concept to production. You’ve owned scoping, architecture, implementation, release, measurement, and iteration—not just one layer of the solution. • Designed distributed systems. You can reason clearly about throughput, failure modes, data flow, scalability, and operational tradeoffs. • Built LLM-powered or agentic features in production. You understand the practical differences between models and can balance capability against latency and cost. • Worked autonomously on ambiguous problems. You know how to gather requirements, ask useful questions, and turn incomplete context into forward motion. • Communicated clearly across teams. You can explain technical tradeoffs to engineers and non-engineers, give direct feedback, and collaborate without creating unnecessary process. • Worked in a fast-moving product environment. You’re comfortable learning quickly, shipping incrementally, and changing direction when the evidence does. Bonus points if you’ve worked with multimodal embeddings, vector databases, semantic search, ranking algorithms, model deployment, self-hosted models, or GPU infrastructure. Curiosity about the creator economy helps too, but we’ll get you up to speed. Our stack • AWS and GCP, with Pulumi for infrastructure as code • Python, TypeScript, and Node.js • PySpark on AWS EMR • Airflow • Milvus Vector DB through Zilliz • Elasticsearch • LLM batch APIs • Apache Iceberg • SageMaker, DynamoDB, S3, Glue, Kinesis, Lambda, ECS, and Aurora • Slack, GitHub, Linear, Notion, and Cursor The interview process We move quickly and can complete the process in under a week: • Intro chat • Coding interview • System design interview • Team interview • Culture and alignment conversation with our CEO, Avery S