ML Product Engineer

<p>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.</p> <h2>Tasks</h2> <ul> <li>Turn research models into production-grade services with clear reliability, latency and cost targets.</li> <li>Build evaluation harnesses and release criteria that show quantitatively when a model is ready to ship.</li> <li>Design the data pipelines, versioning and observability needed across training, evaluation and live inference.</li> <li>Build stable APIs and developer-facing abstractions around our models.</li> <li>Work closely with researchers to expose failure modes and turn product feedback into better models and evaluations.</li> <li>Translate customer and design-partner needs into reusable platform capabilities rather than one-off solutions.</li> <li>Own model releases, monitoring and rollback patterns as the production footprint grows.</li> </ul> <h2>Requirements</h2> <ul> <li>A track record of shipping ML-powered systems to production and operating them after launch.</li> <li>Strong software engineering skills in Python and hands-on fluency with PyTorch.</li> <li>Experience with model serving, APIs, containers and cloud infrastructure.</li> <li>Sound judgment around evaluation, observability, reliability and production trade-offs.</li> <li>The ability to work directly with customers, researchers and product stakeholders.</li> <li>A pragmatic, outcome-oriented mindset: you optimize for dependable capabilities that users can actually adopt.</li> <li>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.</li> </ul> <p>Nice to have:</p> <ul> <li>In-context learning, PFNs, synthetic data or probabilistic models.</li> <li>Weights &#x26; Biases, model registries, CI for models or comparable MLOps tooling.</li> <li>SDK or developer-tooling design.</li> <li>Security, privacy or on-premise deployment requirements.</li> <li>Prior startup, design-partner or 0-to-1 product experience.</li> </ul> <h2>Benefits</h2> <p>🚀 <strong>Where This Can Go</strong></p> <p>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.</p> <p>🫂 <strong>Our Culture</strong></p> <p>We are "Putting Science at the Core of AI". That means we:</p> <ul> <li>are scientists at heart, with a builder's mindset,</li> <li>are open to challenge, grounded in curiosity and respect,</li> <li>welcome diverse perspectives and value thoughtful, open debate,</li> <li>focus on outcomes and real-world impact,</li> <li>foster an environment of support, inspiration, and freedom for everyone to do their best work.</li> </ul> <p>🏆 <strong>Perks &#x26; Benefits</strong></p> <ul> <li>VSOP equity: a real stake in what we build.</li> <li>30 days of paid holiday per year.</li> <li>Statutory social insurance.</li> <li>Conference travel and role-relevant learning.</li> <li>Flexible hybrid work, with roughly one in-person team meet-up per month.</li> <li>A high-end laptop and access to the cloud compute required for the role.</li> </ul> <p>⚒️ <strong>Tools and Infrastructure</strong></p> <ul> <li>Python and PyTorch.</li> <li>Weights &#x26; Biases and model-evaluation tooling.</li> <li>Docker, AWS, RunPod and comparable cloud infrastructure.</li> </ul> <p>🫶 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.</p> <p>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.</p> <p>We are looking forward to hearing from you!</p> <p>Find <a href="https://www.arbeitnow.com">Jobs in Germany</a> on Arbeitnow</a>