Founding Engineer

Almedia

This isn’t your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We’re aiming to become Germany’s second bootstrapped unicorn. Almedia is already Europe’s #3 fastest-growing company in 2025 (FT1000). We are building the future of marketing by rewarding our community of over 70 million users for engaging with our advertisers’ products. We are offering a new way to acquire users for the biggest companies in the world. We’re looking for a Staff Engineer to work directly with leadership on high-impact, zero-to-one projects. If you love taking full ownership, moving fast, and building real products end-to-end, we want to hear from you.   STAFF ENGINEER (€135K – €190K • Offers Equity)   WHAT YOU’LL DO: * Build core product features end-to-end (backend and/or frontend/mobile/web), from idea to production. * Define technical architecture and make system-level decisions that other engineers build on. * Take full ownership of projects: scope, architecture, implementation, and rollout. * Jump onto critical initiatives (SDK, network, new product bets) and unblock them fast. * Ship high-quality, testable code at high speed. * Work closely with whoever is needed to shape what we build next. * Using AI tools (Claude, Cursor, Copilot, etc.) as part of your everyday toolkit — we actively encourage AI-assisted development.   WHAT YOU NEED: * Strong experience as a senior / staff-level engineer in product teams. * Strong backend foundation (e.g. TypeScript/Node); full-stack, mobile, or frontend experience a strong plus. * Proven zero-to-one experience (early-stage startup, founding engineer, or similar). * Track record of owning complex projects and delivering them to production.   YOU ARE: * A builder with a strong ownership mindset and high output. * Comfortable with ambiguity, fast decisions, and a high bar for quality. * Product-minded: you care about impact, not just code. * A clear, direct communicator in English who can align stakeholders and elevate the engineers around you.   WHY ALMEDIA? * Own Our Growth: Competitive package with meaningful equity. * High Impact: Work on company-defining projects, not tickets in a queue. * Scale With Almedia: Join a profitable, high-growth startup early. * Great Environment: Central Berlin office, transport subsidy, breakfasts and lunches, language learning, Urban Sports Club, and more.   We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.

Almedia

This isn’t your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We’re aiming to become Germany’s second bootstrapped unicorn. Almedia is already Europe’s #3 fastest-growing company in 2025 (FT1000). We are building the future of marketing by rewarding our community of over 70 million users for engaging with our advertisers’ products. We are offering a new way to acquire users for the biggest companies in the world. We're looking for an Engineering Working Student to join the founding engineering team building Link, Almedia's newest product. This is a unique opportunity to work directly alongside the Director of Engineering and a small, high-caliber team from the ground up. WORKING STUDENT ENGINEERING (Part-time • 1-year contract) YOU ARE: * An engineering student who wants real product exposure, not a checkbox internship. * Comfortable with ambiguity and a fast-moving early-stage environment. * Ambitious - you want to be the person who knows the product inside out. WHAT YOU’LL DO: * Support Partner Onboarding: Ensure technical setups are functional, verify metrics, and maintain accurate database entries as Link scales its partner network. * Own Testing & QA: We'll literally pay you to play mobile games - bonus awaits if you break them (or our systems) in interesting ways. That's basically QA here: you'll own functional testing across our product, mobile included, and build a sharp instinct for catching issues before users ever see them. * Enable the Engineering Team: Support team members with setup tasks and day-to-day technical needs so they can stay focused on higher-complexity work. * Build Better Workflows: Continuously improve and automate the operational processes you own, reducing manual overhead as the product matures. * Programming Opportunities: As you grow, you'll get chances to contribute hands-on to programming work alongside the team. * Grow Your Technical Depth: Work closely with the Director of Engineering and the founding team, with exposure to how a technical product is built and scaled from early stage.   WHAT YOU'LL BRING 💡 * Engineering Curiosity: You're studying engineering or a related field and are drawn to how systems work in practice. * Reliability: You do repetitive tasks well and consistently, without complaining. The team needs to be able to depend on you for the (un)glamorous work that keeps the product running. * Initiative & Backbone: You ask hard questions, push back when something doesn't make sense, and take ownership without needing to be chased. * Technical Aptitude: Familiarity with JavaScript or TypeScript is a plus - but attitude and aptitude matter far more than your current stack. * Availability: Able to commit to at least a year in the role.   WHY ALMEDIA? * Scale With Almedia: Grow alongside a startup that has been profitable from day one. * Central Berlin Office: Work from a fully-stocked modern office built for collaboration, accessible from all around Berlin. * Clear Path Forward: Strong performers move into a junior developer role at the end of the engagement. We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.

Prior Labs

WHO WE ARE Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched. Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them. Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it. Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical-trial decisions with BostonGene. The hardest work is ahead. We're scaling tabular foundation models to millions of rows, thousands of features, real-time inference, and entirely new data modalities, while building the infrastructure to run them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level. Our team. We're a small, highly selective team of 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar. What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join. ABOUT THE ROLE Tabular data breaks the assumptions that make scaling work for language and vision. There's no natural sequence, no spatial structure, no shared vocabulary across datasets. The architectures and scaling laws that power LLMs don't transfer. We've made the first breakthrough with TabPFN — but the hardest problems are still ahead. At Prior Labs, Research Engineers aren't supporting scientists — they are the science team. You'll design experiments, contribute to papers, and write the code that turns architectural ideas into trained models. We create cutting edge research because the same people do both. As an early team member, you'll have significant technical ownership and room to grow as we scale. The problems we're solving: * Scaling transformer architectures from 10K to 1M+ samples — without the structural assumptions that make language models scale * Building multimodal models that combine tabular, text, and numerical understanding * Making models efficient enough for real-world deployment — not just accurate enough for a paper * Designing architectures for time series, forecasting, anomaly detection, and multiple related tables Day-to-day, you'll design and test novel architectures, run ablations, analyze scaling behavior, and write the training and evaluation infrastructure that makes rapid experimentation possible. We hold software quality to the same standard as research quality. What We're Looking For * Master's or PhD in Computer Science or a related field, plus 3+ years of experience building ML systems in research or industry * Publications at top ML venues (NeurIPS, ICML, ICLR, etc.) or equivalent demonstrated research impact (widely used open-source, deployed systems) * Deep proficiency in Python, PyTorch, and the broader ML and data science ecosystem (scikit-learn, pandas, NumPy), with strong software engineering practices * Experience implementing and training neural network architectures — ideally transformers or foundation models * Solid understanding of training dynamics, scaling behavior, and common failure modes in deep learning systems * Genuine interest in model efficiency — making large models faster, more scalable, and practical to deploy Nice to Have * Experience at an early-stage startup or as a founding engineer * Contributions to open-source ML libraries or tools * Experience with model distillation, inference optimization, or on-device ML * Background in tabular data, time series, or other structured data — helpful but not required Life at Prior Labs We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you. We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together. Our Commitments We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box." We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are. We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.