ML Product Engineer

At kausable, we build causal, reasoning-first models that learn from a handful of examples and generalize across domains. Research gets us to a capable model. This role gets that model into the hands of users. As our ML Product Engineer, you own the path from a promising result in the lab to a dependable production capability: serving, evaluation, data flows, reliability, latency and cost. You will work at the boundary between research and product, where good technical judgment matters more than a clean handover. Tasks ----- * Turn research models into production-grade services with clear reliability, latency and cost targets. * Build evaluation harnesses and release criteria that show quantitatively when a model is ready to ship. * Design the data pipelines, versioning and observability needed across training, evaluation and live inference. * Build stable APIs and developer-facing abstractions around our models. * Work closely with researchers to expose failure modes and turn product feedback into better models and evaluations. * Translate customer and design-partner needs into reusable platform capabilities rather than one-off solutions. * Own model releases, monitoring and rollback patterns as the production footprint grows. Requirements ------------ * A track record of shipping ML-powered systems to production and operating them after launch. * Strong software engineering skills in Python and hands-on fluency with PyTorch. * Experience with model serving, APIs, containers and cloud infrastructure. * Sound judgment around evaluation, observability, reliability and production trade-offs. * The ability to work directly with customers, researchers and product stakeholders. * A pragmatic, outcome-oriented mindset: you optimize for dependable capabilities that users can actually adopt. * We are primarily hiring at senior level. We are also open to exceptional candidates with fewer years of experience who can demonstrate comparable depth, judgment and ownership. Nice to have: * In-context learning, PFNs, synthetic data or probabilistic models. * Weights & Biases, model registries, CI for models or comparable MLOps tooling. * SDK or developer-tooling design. * Security, privacy or on-premise deployment requirements. * Prior startup, design-partner or 0-to-1 product experience. Benefits -------- 🚀 **Where This Can Go** You will define how kausable ships ML: the patterns, tooling and standards between research and production. As the team grows, the role can expand into technical ownership of the model-to-product stack or leadership of a small ML product group. The trade-off is part of the job: shipping quickly matters, but only when the resulting system remains measurable, reusable and dependable. 🫂 **Our Culture** We are "Putting Science at the Core of AI". That means we: * are scientists at heart, with a builder's mindset, * are open to challenge, grounded in curiosity and respect, * welcome diverse perspectives and value thoughtful, open debate, * focus on outcomes and real-world impact, * foster an environment of support, inspiration, and freedom for everyone to do their best work. 🏆 **Perks & Benefits** * VSOP equity: a real stake in what we build. * 30 days of paid holiday per year. * Statutory social insurance. * Conference travel and role-relevant learning. * Flexible hybrid work, with roughly one in-person team meet-up per month. * A high-end laptop and access to the cloud compute required for the role. ⚒️ **Tools and Infrastructure** * Python and PyTorch. * Weights & Biases and model-evaluation tooling. * Docker, AWS, RunPod and comparable cloud infrastructure. 🫶 Sounds like it's for you? Send us your favorite way to drink coffee along with your CV or LinkedIn, and we'll get back to you soon. If it's a match, we'll get to know each other over a number of online interviews, followed by an onsite day where we go in depth. We are looking forward to hearing from you! Find [Jobs in Germany](https://www.arbeitnow.com) on Arbeitnow