Applied ML Engineer

Jobgether·Francemid

In short

→Ingeniero de ML aplicado que transforma investigaciones en experimentos y productos confiables.
→Día a día: evalúa modelos, construye infraestructura de pruebas, desarrolla herramientas para visualizar resultados y prueba métodos de verificación en modelos
→Destacado: debes reproducir y documentar al menos un método de verificación de modelos publicado en los primeros 6 meses.

Strong written and spoken English proficiency required.

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What they ask for

✓Firmes habilidades en Python con experiencia práctica en PyTorch y Hugging Face Transformers.
✓Comprensión sólida de evaluación ML: diseño de datasets, métricas, calibración, sesgos y reproducibilidad.
✓Capacidad para leer y reproducir métodos de investigación de ML desde cero, sin depender solo de paquetes existentes.
✓Experiencia profesional en ingeniería de software más allá de notebooks: APIs, jobs asíncronos, bases de datos, testing y documentación.
✓Conocimiento práctico de modelos de peso abierto y sistemas de inferencia de LLMs modernos.
✓Capacidad para trabajar entre backend y frontend, con conocimientos básicos de React/TypeScript para integrar experiencias de usuario.

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PythonPyTorchHugging Face TransformersReactTypeScriptAPIsasynchronous jobsdatabasesloggingtesting

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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 France. 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-verification runner with versioned inputs, artifacts, metrics, and reports, and make at least one verification workflow accessible through the product interface. Run controlled experiments across base, fine-tuned, merged, quantized, and known distilled models, improving understanding of when verification methods succeed, fail, and why. Requirements: Strong 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. Familiarity with open-weight models and a practical understanding of how modern LLM inference systems operate. 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.

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