Applied ML Engineer

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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied ML Engineer based in Switzerland. As an Applied ML Engineer, you’ll work at the intersection of machine learning research, experimentation, and production engineering. You’ll turn ideas from research papers into rigorous experiments, measurable evidence, and reliable products.

The role spans model evaluation, model internals, inference infrastructure, backend systems, and user-facing product experiences. You’ll work hands-on with modern ML models, designing evaluations that reveal what methods can—and cannot—demonstrate. You’ll also build production-grade tooling that makes complex experiments repeatable, observable, and accessible to users. The environment values technical judgment, ownership, scientific rigor, and the ability to move comfortably across the technology stack.

It’s an opportunity to help transform emerging ML techniques into practical systems that people can trust. Accountabilities Reproduce and evaluate machine learning research methods using open-weight and API-accessible models. Design evaluation datasets, probes, scoring approaches, baselines, calibration tests, and experiment harnesses. Work directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure when required.

Build and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, reporting, and reproducibility. Turn research workflows into intuitive product experiences, including experiment configuration, execution, traces, comparisons, reports, and review workflows. Investigate how verification methods behave when models are modified through fine-tuning, merging, quantization, distillation, safety removal, or deliberate evasion.

Design controlled experiments that distinguish meaningful signals from artifacts, confounders, and misleading correlations. Produce clear technical reports that separate measured evidence from interpretation and hypotheses. Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, deployment, and documentation. Contribute across research, experimentation, engineering, and product as priorities evolve.

During the first six months, reproduce and document at least one published model-provenance or verification method, including its capabilities, assumptions, and limitations. Build a repeatable model