Modash

Remote — Data Core Team — Full-time Hey, I'm Andrei . I’m hiring a Senior Backend Engineer to join the Data Core 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. Every one of those products depends on fresh, reliable creator data. Data Core owns the systems that collect and maintain profiles for 400M+ creators across social platforms, process billions of data points, and keep the rest of Modash supplied with the raw material it needs. That’s where you come in. Why we're hiring Data Core isn't an internal support team. It owns the collection infrastructure that the entire Modash product—and every downstream data team—depends on. Collecting social data at this scale is a hard distributed-systems problem. APIs change without warning. Providers become unreliable. Platforms rate-limit requests. Data grows stale. A tiny inefficiency becomes expensive when repeated hundreds of millions of times. We need a senior engineer who can design resilient services, make thoughtful tradeoffs between coverage, freshness, reliability, and cost, and take production-critical systems from a rough idea to dependable operation. You’ll join the specialised Data Core team and work closely with Data Search, Data Insights, product teams, and company leadership. You’ll have real ownership, 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. Keep creator data flowing at massive scale. You’ll build and evolve the systems that collect and maintain 400M+ creator profiles across Instagram, TikTok, and YouTube—keeping billions of data points fresh enough for search, analytics, APIs, and customer-facing products. 2. Make collection resilient when the outside world isn't. You’ll design services that handle third-party API instability, rate limits, provider outages, platform changes, and partial failures without turning every disruption into a customer incident. 3. Improve the economics of collection. At this scale, every request, proxy call, retry, storage decision, and compute cycle matters. You’ll improve coverage and freshness while keeping the system financially sustainable. 4. Own production, not just the code. You’ll shape the problem, design the architecture, write and review the code, ship it, observe it, and improve it. You’ll build the monitoring and operational safeguards that catch gaps and regressions before customers do. What the day-to-day looks like Here’s what a typical week might include: Monday. A social platform has changed its behaviour overnight. Collection success has dropped, but only for part of the traffic. You trace the failure pattern, protect downstream freshness, and design a resilient fix rather than a brittle patch. Tuesday. Deep-focus time. You redesign part of the subscription system that decides which creators to collect, when, and how often—balancing customer value against request and compute cost. Wednesday. You pair with a Data Search engineer on an indexing dependency. Together, you agree on a cleaner contract that improves freshness without coupling the two teams’ systems. Thursday. You review a new request-routing approach across proxy providers. You model throughput, failure modes, and unit economics before shipping a small production experiment. Friday. An observability review reveals a slow coverage regression that existing alerts missed. You improve the data-quality checks so the team catches the next one before it reaches customers. 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, harden, and operate systems. Requirements What you've done before Built large-scale backend or data systems. You have solid experience with high-volume services where reliability, throughput, latency, and cost all matter. Shipped systems from concept to production. You’ve owned scoping, architecture, implementation, release, operation, and iteration—not just one layer of the solution. Designed distributed systems. You can reason clearly about partial failure, retries, idempotency, backpressure, scaling, and operational tradeoffs. Built resilient integrations. You’ve worked with third-party APIs or other external dependencies that are rate-limited, unstable, or liable to change. Worked with proxy management or request routing. You understand the practical challenges of routing high-volume traffic and navigating anti-bot systems responsibly at scale. Taken operational ownership. You care about observability, alerting, runbooks, and what happens after deployment—not just whether the code merged. Worked autonomously on ambiguous problems. You know how to gather requirements, ask useful questions, and make progress without wait

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 unnecessary process. Worked in a fast-moving product environment. Y