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.

It's an opportunity to help transform emerging ML techniques into practical systems that people can trust. AccountabilitiesReproduce 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.

Turn research workflows into intuitive product experiences, including experiment configuration, execution, traces, comparisons, reports, and review workflows. Design controlled experiments that distinguish meaningful signals from artifacts, confounders, and misleading correlations. Deliver production-quality systems with APIs, asynchronous jobs, databases, observability, testing, deployment, and documentation.

Contribute across research, experimentation, engineering, and product as priorities evolve. Run controlled experiments across base, fine-tuned, merged, quantized, and known distilled models, improving understanding of when verification methods succeed, fail, and why. RequirementsStrong Python engineering skills, with hands-on experience using PyTorch and Hugging Face Transformers.

Solid understanding of machine learning evaluation, including dataset design, baselines, metrics, calibration, false positives and negatives, statistical uncertainty, and reproducibility. Ability to read ML research papers critically and implement methods from first principles rather than relying entirely on existing packages. Professional software engineering experience beyond notebooks, including APIs,



asynchronous jobs, databases, logging, testing, deployment, and documentation.

Ability to work across backend and frontend boundaries, with sufficient React/TypeScript knowledge to help make complex experiments and results understandable to users. Strong experimental and analytical judgment, particularly around distinguishing what evidence demonstrates from what it merely suggests. High ownership and initiative, with the ability to identify problems, propose solutions, and drive projects forward independently.

Experience with model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluation, interpretability, or related areas is a plus.

Experience with activation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals techniques is advantageous. Familiarity with evaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, or comparable technologies is beneficial.

Experience with Next.js, React, TypeScript, data visualization, or experiment dashboards is a plus.

Experience designing adversarial evaluations or testing systems against deliberate attempts to evade detection is an advantage. A strong commitment to producing production-quality code, tests, tooling, and documentation that other engineers can confidently operate and extend. BenefitsOpportunity to work on applied machine learning at the intersection of research, experimentation, engineering, and product.

End-to-end ownership across model evaluation, model internals, infrastructure, backend systems, and user-facing experiences. Opportunity to translate cutting-edge research into practical, measurable, and user-accessible products. Additional compensation, flexibility, healthcare, and other benefits may be provided according to the partner company's employment package and local arrangements.

📌 Applied Machine Learning Engineer (Madrid)
🏢 Jobgether
📍 Madrid