Research Scientist - Model Team

Mirelo

Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that "unmute" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role At Mirelo, we’re pushing the limits of what generative audio can do, and our ability to innovate depends heavily on the quality of our underlying audio representations. In this role, you’ll work at the core of our modeling stack - designing, training, and evaluating neural audio codecs that directly shape the performance of our next-generation music and sound models. You’ll collaborate closely with the model team, experiment with both continuous and discrete representations, and build the evaluation tools that help us understand what actually moves the needle. Your work will sit at the foundation of building the best-sounding generative models in the world. KEY RESPONSIBILITIES * Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models. * Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context of generative audio models (in the end, the ultimate goal is to build the best audio generative models) * Explore the trade-offs of continuous (as typically used for diffusion models) vs. discrete audio representations (as typically used for autoregressive models). * Develop benchmarking pipelines for codec evaluation * Conduct initial experiments with generative models to verify that a new candidate codec is actually useful for our downstream tasks. IDEAL CANDIDATE PROFILE * Strong background in deep learning for audio: neural codecs, source separation, speech models, or generative audio systems * Specific hands-on experience in designing and training neural audio codecs * Solid understanding of audio signal processing fundamentals * Strong track record (research and/or open-source) in the field of audio ML NICE TO HAVE * Hands-on experience with generative audio models and good intuition of how the choice of the codec influences the training and performance of the generative model * Strong publication record (e.g., NeurIPS, ICML, ICLR, Interspeech, ICASSP, WASPAA) WHY JOIN? * Join at a pivotal moment. We've secured fresh funding and are gaining traction - now is when your contributions can make a real difference to our success. * True ownership from day one. You'll have genuine autonomy and responsibility. Your ideas and work will directly shape our product and company direction. * Competitive compensation and equity. We offer strong packages that ensure you share in the success you help create. * Build for the next generation of creators. Be part of the innovation that will transform how creators work and thrive. We welcome applications from all individuals, regardless of ethnic origin, gender, disability, religion or belief, age, or sexual orientation and identity.

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. CORE AREAS OF IMPACT You'll be among the first scientists collaborating and working an entirely new class of AI models, not just incremental improvements. As an early-stage startup working on foundation models for tabular data, we have countless exciting research ideas and problems to explore - you're sure to find challenges that match your interests and expertise. We are working on problems such as: * Scaling our transformer architectures from 10K to 1M+ samples while maintaining performance * Building multimodal models that combine text and tabular understanding on proprietary data * Developing specialized architectures for time series, forecasting, and anomaly detection * Creating efficient inference methods for production deployment * Researching causal understanding in foundation models * Designing novel approaches for handling multiple related tables WHAT WE'RE LOOKING FOR * Currently pursuing or holding a PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related field (we will also consider exceptional Master's students) * Deep experience with ML frameworks, especially PyTorch and scikit-learn * Strong engineering fundamentals with excellent Python expertise * Experience in data-science and working with tabular data or time series * Publications at top-tier venues (NeurIPS, ICML, ICLR) or significant open-source contributions BENEFITS * Strong mentorship and professional development opportunities * Work with state-of-the-art ML architecture, substantial compute resources, and a world-class team * Comprehensive benefits including healthcare, transportation, and fitness 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.

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. What you'll do This role is foundational data science: building the foundations of tabular foundation models so a single model can solve data-science problems across the board. Roughly half the work is inventing new frontier tools for TFMs, and half is building the dataset and benchmark bedrock they stand on. * Invent and build the frontier tools that extend TabPFN, including its thinking, scaling, and agentic capabilities, and the new methods that let one model generalize across the full landscape of data-science problems. This is the most open-ended part of the work and grows over time. * Set the research direction by deciding which model capabilities and benchmarks are worth pursuing, choosing what is worth solving rather than optimizing a score someone else set. * Bring in external research and real customer needs to shape new model and tooling directions, and publish frontier results that move the field forward. * Build trustworthy benchmarks from the structured data behind real, high-impact problems, so the team optimizes for real-world performance rather than one leaderboard. * Faithfully implement the baselines and competitor models that set the gold standard of applied data science, giving the team a read on where TabPFN leads and where there is room to improve. * Build an automated, agentic pipeline with a human in the loop so this data and benchmark foundation scales to far larger volumes without losing rigor, itself a genuinely new tool. What we're looking for * You have solved data-science problems across many domains and datasets to a high standard, optimizing for strong performance across a whole suite of tasks rather than the single best score on one. * You work undogmatically across the ML toolbox, including getting strong results with gradient-boosted trees (such as XGBoost) and not only with deep learning. * You understand the common categories of dataset defects (leakage, label noise, distribution shift, duplication, mislabeled targets, and similar) and why each corrupts a training or benchmark signal. * You are energized by foundational work, valuing the dataset and benchmark bedrock as much as the frontier tooling, and you have taken on hard problems others passed over. * You thrive as a senior individual contributor in an ambiguous, early-stage, low-process environment. You are opinionated on best practice in Data Science and can make good judgement calls on approaches to complex problems. Nice to have * Experience building or extending evaluation harnesses, benchmark suites, or experiment frameworks that others rely on. * Experience building LLM- or agent-assisted pipelines with a human in the loop to scale a previously manual workflow. * Experience acting as the link between external research or customer needs and an internal model or product roadmap. * Prior work on tabular, structured-data, or foundation-model problems, or helping shape an emerging research subfield through community work. 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.