Senior Software Engineer (Data Search) at Modash

Hey, I'm Adrian, 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. Over 2,700 companies, including Stanley 1913, Sennheiser, and NordVPN, use Modash to manage and scale their creator partnerships.

The core challenge involves helping customers find the right creators across 400M+ profiles and billions of media files. This requires combining large-scale data processing, traditional retrieval, vector search, multimodal embeddings, and LLMs.

Why we're hiring

Search at Modash is a core product customers rely on for discovering creators. The scale is massive, the data is messy, and search intent is often complex. You’ll tackle ambiguous product problems end-to-end, from data pipelines to low-latency serving.

You’ll collaborate closely with Data Core, Data Insights, product teams, customers, and leadership. Expect real autonomy with strong team support.

For a deeper look at our approach, check our Engineering Blog.

What you'll actually own

1. Make creator search meaningfully better.

You’ll enhance how customers discover creators across 400M+ profiles and billions of media files, focusing on retrieval, filtering, ranking, relevance, speed, and product decisions.

2. Turn multimodal data into searchable intelligence.

Build systems generating and utilizing embeddings from images, video, text, and audio at scale, ensuring these signals are actionable in customer-facing search.

3. Ship new search capabilities into production.

Evaluate models and technologies pragmatically, balancing cost, latency, and quality, and deploy promising solutions within weeks, not quarters.

4. Own the system end to end.

Shape problems, gather requirements, design architecture, write code, release, measure outcomes, and iterate—senior engineers here drive results, not just implementation.

What the day-to-day looks like

Here’s a typical week:

• Monday. Investigate and improve a customer search returning technically relevant but unhelpful results.
• Tuesday. Build a pipeline for multimodal embeddings, testing its impact on retrieval quality and cost.
• Wednesday. Collaborate with Data Insights to define and expose new datapoints for customer discovery.
• Thursday. Test a reranking model, measuring relevance improvements against latency and inference costs.
• Friday. Review production metrics, diagnose relevance regressions, and share insights with the team.

Meetings are purposeful, and deep work is protected. Expect short standups, close collaboration when needed, and ample time for design, building, optimization, and launching.

Requirements

What you've done before

• Built large-scale data or backend products. Experience with systems prioritizing volume, latency, reliability, and cost.
• Shipped products from concept to production. Owned full lifecycle: scoping, architecture, implementation, release, measurement, and iteration.
• Designed distributed systems. Reasoned about throughput, failure modes, scalability, and operational tradeoffs.
• Built LLM-powered or agentic features in production. Understands model differences and balances capability against latency and cost.
• Worked autonomously on ambiguous problems. Skilled in gathering requirements, asking useful questions, and progressing with incomplete context.
• Communicated clearly across teams. Explains technical tradeoffs to engineers and non-engineers, provides direct feedback, and collaborates efficiently.
• Worked in a fast-moving product environment. Comfortable with rapid learning, incremental shipping, and pivoting based on evidence.

Bonus points for experience with multimodal embeddings, vector databases, semantic search, ranking algorithms, model deployment, self-hosted models, or GPU infrastructure. Curiosity about the creator economy is a plus.

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, completing the process in under a week:

1. Intro chat
2. Coding interview
3. System design interview
4. Team interview
5. Culture and alignment conversation with CEO, Avery Schrader

Benefits

What we offer

• Fully remote in Europe 🏠 Work from anywhere with GMT+3 overlap.
• Compensation. Salary range: 100,000€ - 130,000€ annually, including stock options. Exact amount depends on location, employment type, skills, and experience.
• Flexible hours ⏱ Focus on outcomes, not hours logged.
• Unlimited paid vacation 🌴 Take time off as needed.
• Personal development support 🧠 Access to courses, books, and conferences.
• Real ownership 💡 Solve complex problems from idea to production with minimal process.
• Regular offsites ✈️ Connect, collaborate, and have fun in person.