Postdoc, research in LLM engineering i.e

Le poste de postdoctorant en ingénierie LLM à l'EPFL. Une chance de rejoindre une équipe dynamique en santé.

Tâches

• Conduire la recherche sur des modèles LLM médicaux open-source.

• Développer des pipelines LLM évolutifs et sécurisés.

• Collaborer avec des cliniciens pour assurer l'impact global.

Compétences

• Expérience en formation de modèles avec infrastructure distribuée.

• Expertise en PyTorch et optimisation des transformateurs.

• Compétences en communication scientifique et publication.

Postdoc, research in LLM engineering i.e

Swiss Federal Institute of Technology Lausanne, EPFL

Workplace Lausanne, Lake Geneva region, Switzerland Category Health | Research Management

Position Senior Scientist / Postdoc

Published 18 June 2025 Apply now »

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.

postdoc, research in LLM engineering i.e.

Mission

We’re hiring a technically exceptional and impact-focused Postdoctoral Researcher in LLM engineering to lead research and development within the Meditron initiative

a suite of evolving open-source medical LLMs and multimodal foundation models.

You will contribute to model training, data curation, safety alignment, and global health deployments. You’ll join a fast-moving, interdisciplinary team of researchers, engineers, and students advancing trustworthy AI for medicine and humanitarian response.

Main duties and responsibilities

• Design scalable LLM pipelines using FSDP, DeepSpeed, HuggingFace Accelerate

• Lead model development (e.g., LLaMA, Mistral, Phi, Gemma) using LoRA, FlashAttention-2, MoE

• Contribute to safety alignment (RLHF, DPO, red-teaming, rejection sampling, calibration)

• Support data pipeline audits (tokenizer design, deduplication, privacy, synthetic supervision)

• Benchmark across general and medical tasks (lm-eval-harness, HELM, guideline adherence)

• Publish and present research at top venues (e.g., NeurIPS, ICLR, ML4H)

Collaborate closely with clinicians and humanitarian partners to ensure safety and usability

Profile

• Proven experience training 1B parameter models with distributed infrastructure

• Expertise in PyTorch, HuggingFace Transformers, DeepSpeed, FSDP

• Deep understanding of transformer internals and optimization strategies

• Strong Python engineering skills (tests, containers, CI/CD, reproducibility)

• Clear scientific communication and publication experience

Preferred

• Familiarity with clinical LLMs or decision support systems

• Experience with safety-critical evaluation (e.g., hallucination detection, benchmark leakage)

• Contributions to open-source projects

• Passion for equity-centered deployment and global health

Our Stack

PyTorch, HuggingFace, DeepSpeed, FSDP, WANDB, Hydra, MLFlow, WebDataset, Slurm, Docker, lm-eval-harness, OpenCompass, MedQA, SwissAlps (CSCS)

We offer

At LiGHT, we combine scientific rigor with purpose-driven action. We value creativity, humility, collaboration, and principled research that leads to tangible health impact. Our team culture embraces diverse backgrounds and sustained commitment to excellence, equity, and integrity.

Informations

Only applications submitted through the online platform are considered. You are asked to supply:

• A brief cover letter (pdf, up to 2 pages).

And in one PDF:

• A CV with a publication list.

• A research statement (pdf, up to 3 pages).

• Contact details for 3 referees.

For any further information, please contact: [XXX]

Contract Start Date :

Activity Rate : 100.00

Contract Type:CDD

Duration: 1 year, renewable

Reference: 1635

Apply now »

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More information

In your application, please refer to myScience.ch and referenceJobID67608.