Applied ML Engineer: From Research to Production

Jobgether SRL; Lavamünd; Hybrid; EUR 60.000 - 90.000; Vollzeit

Benefits dieser Stelle

Location: Romania

Healthcare

Flexible arrangements

Zusammenfassung

We are seeking an Applied ML Engineer to work at the intersection of machine learning research, experimentation, and production engineering. The role involves turning research ideas into rigorous experiments and reliable products, spanning model evaluation to production‑grade tooling and user‑facing experiences.

Responsibilities include designing evaluation datasets, working with model weights and inference infrastructure, and delivering reproducible, production‑quality systems with APIs,

Qualifikationen

• 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/negatives, and reproducibility.
• Professional software engineering experience beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation.

Aufgaben

• Reproduce and evaluate machine learning research methods using open-weight models and APIs.
• Design evaluation datasets, probes, scoring, baselines, calibration tests, and experiment harnesses.
• Build and extend evaluation infrastructure covering experiment runners, judges, persistence, orchestration, and reporting.

Kenntnisse

Python engineering

PyTorch

Hugging Face Transformers

Machine learning evaluation

React/TypeScript

Experimentation

Open-weight models

Inference infrastructure

SQL/Databases

Software engineering

Tools

PyTorch

Hugging Face Transformers

Next.js

Temporal

vLLM

Jobbeschreibung

We are seeking an Applied ML Engineer to work at the intersection of machine learning research, experimentation, and production engineering. The role involves turning research ideas into rigorous experiments and reliable products, spanning model evaluation to production‑grade tooling and user‑facing experiences.

Responsibilities include designing evaluation datasets, working with model weights and inference infrastructure, and delivering reproducible, production‑quality systems with APIs,