ML Engineer, Backend

About the Position

ML Engineer, Backend

---

About the Role

You will have ownership over designing, building, and scaling the core systems that bring Prior Labs' foundation models to the world. This is a unique opportunity to make fundamental architectural decisions, establish engineering best practices from the ground up, and profoundly shape the technical direction for serving state-of-the-art AI.

TabPFN does its learning in the forward pass, and it now handles datasets in the millions of rows. Making that fast, reliable, and affordable is an exciting engineering challenge.

You'll work directly with world-class AI researchers, translating cutting-edge models into scalable production systems. This role offers significant autonomy and impact, with clear paths to specialize in areas you're passionate about (like inference optimization and core backend systems) or grow into a technical leadership position as our team expands. You won't just be implementing features; you'll be building the backbone of our company.

---

Responsibilities

• Own Inference: Own the serving path end-to-end: latency, throughput, batching, memory behavior on large inputs, and cost per prediction. Our models serve through our managed API, inside customer VPCs, and as self-hosted open weights, and you’ll have an impact across the board.
• Architect & Design: Design robust, scalable, and secure backend systems and production-grade APIs for serving our foundation models.
• Build & Implement: Develop high-quality, maintainable code for core backend services.
• Own Infrastructure: Design, deploy, and manage core infrastructure on cloud platforms, focusing on reliability, monitoring, observability, and cost-efficiency.
• Ensure Compliance & Security: Implement secure, GDPR-compliant systems, including data storage, access control, usage tracking, and quota management.
• Champion Best Practices: Drive high standards for testing, CI/CD, documentation, and security within the engineering team.

---

Requirements

• 3+ years of professional experience in machine learning and backend engineering, with a proven track record of managing production infrastructure.
• Proven experience deploying and operating machine learning models in production, with a strong understanding of how models behave and fail under real traffic.
• Strong, hands-on experience designing and building production-grade APIs and backend services.
• Significant experience building and operating services on cloud platforms, with GCP preferred.
• Strong, hands-on experience with Infrastructure as Code (IaC) using tools like Terraform.
• Significant experience with containerization and orchestration technologies (Docker, Kubernetes).
• Proficiency in Python.

---

Nice to Have

• Experience building or managing infrastructure specifically for machine learning (MLOps, model serving frameworks, feature stores, data pipelines).
• Deep experience with inference optimization (quantization, compilation, dynamic batching, caching strategies, interplay with distributed computing).
• Experience with PyTorch and FastAPI specifically.
• Contributions to relevant open-source projects.

---

We Offer

• Opportunity to work with a highly selective, world-class team of AI researchers and engineers.
• On-site work in Berlin, Freiburg, or New York, with occasional remote work options.
• Frequent company-wide offsites to foster collaboration and innovation.
• Commitment to diversity and inclusion, providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.

---

About the Company

Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables.

We pioneered tabular foundation models: TabPFN v2 was a Nature cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level.

We're a small, highly selective team of 40+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun.

In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.