Senior Software Engineer - Data Search (remote, Europe)

Modash; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Modash is hiring a Senior Software Engineer for the Data Search team. You’ll own retrieval, rankings, and embedding-based search across hundreds of millions of creator profiles and media items, building scalable systems and production-ready features.

You’ll drive end-to-end design, implement large-scale pipelines, and collaborate with Data Core, Data Insights, and product teams. Join a team that values autonomy and practical, production-focused engineering.

Qualifications

• Experience building large-scale data or backend systems.
• Shipped concepts to production with end-to-end ownership.
• Designed distributed systems with throughput and latency tradeoffs.
• Experience with LLM-powered features in production.
• Ability to work autonomously on ambiguous problems.

Responsibilities

• Improve creator search across 400M+ profiles and media with better retrieval, ranking, and relevance.
• Build embeddings pipelines for multimodal data at scale and apply signals to search.
• Evaluate models and move promising approaches into production within weeks.
• Own the system end-to-end from concept to release and measurement.
• Collaborate with Data Core, Data Insights, product teams and leadership.

Skills

Large-scale data systems

Backend engineering

Search systems

LLM features

Distributed systems

Job description

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.

Requirements

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 …