Applied ML Engineer: Research-to-Production MLOps

Jobgether SRL; France; Sur place; EUR 85 000 - 120 000; Plein temps

Avantages offerts par ce poste

Location: France

Healthcare

Flexible work arrangements

Résumé du poste

Jobgether SRL is seeking an Applied ML Engineer based in France to bridge ML research, experimentation, and production engineering. You will convert ideas from research into rigorous experiments, building robust evaluation pipelines and production-grade tooling for repeatable experiments.

The role covers model evaluation, internals, inference infrastructure, backend systems, and user-facing experiences, with a focus on open-weight models, LLM inference, and verifiable results.

Qualifications

• Strong Python engineering skills with hands-on PyTorch experience.
• Experience with ML evaluation, datasets, baselines, metrics, calibration, false positives/negatives, and reproducibility.
• Professional software engineering experience beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, deployment, and documentation.
• Familiarity with open-weight models and practical knowledge of LLM inference systems.
• Ability to read ML research papers and implement methods from first principles.

Responsabilités

• 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.

Connaissances

Python

PyTorch

Transformers

React

TypeScript

Outils

PostgreSQL/pgvector

DSPy

LiteLLM

Temporal

Ray

vLLM

Description du poste

Jobgether SRL is seeking an Applied ML Engineer based in France to bridge ML research, experimentation, and production engineering. You will convert ideas from research into rigorous experiments, building robust evaluation pipelines and production-grade tooling for repeatable experiments.

The role covers model evaluation, internals, inference infrastructure, backend systems, and user-facing experiences, with a focus on open-weight models, LLM inference, and verifiable results.