Machine Learning Engineer

DeepJudge, founded by former Google search engineers with PhDs in AI from ETH Zurich, builds enterprise‑search and AI infrastructure that lets legal teams unlock and apply internal knowledge. Their platform, used by leading law firms worldwide, combines search quality with AI‑driven automation to make fragmented expertise instantly accessible. The Machine Learning Engineer will own the full lifecycle of ML‑enabled product components, from data exploration and model research to production deployment and continuous improvement. Core duties involve turning the latest AI research into robust, scalable services, training and fine‑tuning diverse models, and ensuring efficient inference and reliable operation across the platform. A key focus is advancing search performance by applying innovative models, data pipelines, evaluation techniques, and agentic systems both offline and online. To succeed, candidates must have solid hands‑on experience creating and serving deep‑learning models and be fluent in at least one modern framework such as PyTorch, JAX, or TensorFlow. They should be well‑versed in contemporary ML engineering practices and have a track record of building scalable AI solutions that deliver tangible value. Strong communication abilities are essential for explaining complex ideas, giving feedback, and collaborating across engineering and business teams. Additional advantages include prior work with neural‑retrieval or agentic systems and a history of publishing in top AI venues. The role is based in an office near Zurich's central station, with a predominantly on‑site work model. From day one, engineers receive full ownership of their projects, a stake in shaping a high‑impact scale‑up, and a fast‑paced learning environment that blends multiple ML and software‑engineering disciplines. DeepJudge promotes an inclusive culture and welcomes applicants from all backgrounds. What the role asks for: - Hands‑on experience training and serving deep‑learning models - Proficiency in a modern deep‑learning framework (PyTorch, JAX, TensorFlow) - Familiarity with current ML engineering practices - Experience building scalable AI‑powered systems - Exceptional communication and collaboration skills - Experience with neural‑retrieval or agentic systems (nice‑to‑have) - Publications in top AI journals or conferences (nice‑to‑have)