EL

Post-Training Engineer - Apertus

EPFL – École polytechnique fédérale de Lausanne•Engineering
LausanneVollzeit100% Remotevor 3 Wochen

Muslimfreundliche Arbeitsplatz-Merkmale

  • Gebetsraum
gefüllt = im Anzeigentext genanntumrandet = Angabe auf der Website des Arbeitgebers

Rahmen: Homeoffice möglich

Merkmale sind Hinweise aus der Stellenanzeige, keine bestätigten Zusagen. Arbeitgeber-Angaben stammen von der Website des Arbeitgebers, nicht aus dieser Anzeige.

Ich bin interessiertPer WhatsApp teilen
Kostenlos registrieren – das bringt dir ein Konto
  • Alle 1.200+ belegten muslimfreundlichen Stellen durchblättern
  • Beleg-Details sehen: Zitat und Quelle zu jedem Merkmal
  • Jobs merken und Bewerbungen verfolgen
  • Persönliche Empfehlungen nach deinen Werten
Jetzt kostenlos registrieren

🔔 Neue Jobs wie dieser — direkt in dein Postfach

Kein Account nötig. Maximal eine E-Mail pro Woche, Abmeldung jederzeit.

Mit dem Absenden stimmst du zu, E-Mails zu passenden Jobs von SalamJobs zu erhalten. Abmeldung jederzeit möglich.

💬 Oder lass Rafik suchen — dein kostenloser Karriereberater →

📄Stellenbeschreibung

Introduction

The Apertus project, a joint effort between EPFL and ETH Zürich, is seeking a practical and motivated engineer to help build the next generation of open foundation models.

The successful candidate will help develop and run post-training and reinforcement learning pipelines for the Apertus project.

Apertus is trained and developed on Alps, the Swiss National Supercomputing Centre’s supercomputing infrastructure.

The role requires someone who is comfortable working in an HPC environment and collaborating with researchers and infrastructure engineers.

Main duties and responsibilities

The engineer will contribute to the development, execution, and evaluation of scalable post-training workflows for Apertus.

Infrastructure and systems engineering

Build and maintain containerized environments for LLM post-training and RL workloads.
Adapt containers and dependencies for execution on Alps / CSCS infrastructure.
Run and monitor Slurm-based training and evaluation jobs.
Debug failures related to distributed execution, checkpointing, filesystem performance, networking, and GPU utilization.
Help maintain reproducible training recipes, configuration files, launch scripts, and documentation.
Work with researchers and CSCS engineers to improve the reliability and performance of large-scale experiments.

LLM post-training and Reinforcement Learning

Support SFT, preference optimization, and reinforcement learning workflows.
Build and run RL environments for tasks with verifiable outcomes, such as mathematics, code, tool-use, and reasoning.
Develop reward modeling, reward calibration and verifier-based training.
Generate and validate synthetic or gym training tasks.
Run ablation studies comparing algorithms, reward functions, data mixtures, hyperparameters, and infrastructure settings.
Evaluate model behavior across reasoning, coding, mathematics, instruction-following, multilingual, tool-use, and safety benchmarks.
Debug common post-training issues, including optimization instability, reward hacking, regressions, and evaluation failures.

Profile

Essential

MSc or PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field. Exceptional BSc candidates with strong engineering experience will also be considered.
Experience in AI and neural network architectures
Strong collaboration and communication skills and ability to work across research and engineering teams.

Strongly preferred

Experience with Slurm or another HPC workload manager.
Experience building or adapting containers for HPC or GPU clusters.
Experience with LLM fine-tuning, post-training, preference optimization, or reinforcement learning.
Familiarity with distributed training concepts such as data parallelism, tensor parallelism, pipeline parallelism, checkpointing, and GPU communication.
Experience with frameworks such as veRL, slime, Megatron-LM, DeepSpeed, TRL, vLLM, SGLang, or similar tools.

Nice to have

Experience with RL for LLMs, online policy optimization, reward modeling, or RLVR.
Experience creating verifiable tasks for mathematics, code, reasoning, or tool use.
Familiarity with lower-level GPU/distributed libraries such as NCCL, Transformer Engine, FlashAttention, or communication backends.
Experience with large-scale evaluation pipelines.

We offer

A stimulating academic environment at one of the world's leading technical universities
The opportunity to work with state-of-the-art supercomputing infrastructure and cutting-edge AI research
Collaboration with top researchers and engineers from EPFL, ETH Zürich, CSCS, and other Swiss institutions
Flexible working arrangements, including options for remote work
Professional development opportunities, including conference attendance and specialized training
The chance to contribute to open-source projects with global impact
Access to the broader Swiss academic ecosystem and industry partnerships
Being part of Switzerland's sovereign AI development, working on technology with national significance
The role can be based either in Lausanne at EPFL or in Zürich at ETH Zürich.

Informations

Contract Start Date: as soon as possible

Activity Rate Min: 80.00

Activity Rate Max: 100.00

Contract Type: CDD

Duration: 1 year, renewable

Reference: 2192

Job-Übersicht

Veröffentlicht
22. September 2026
Standort
Lausanne
Beschäftigungsart
Vollzeit
Arbeitsmodell
100% Remote

Unternehmen

EL

EPFL – École polytechnique fédérale de Lausanne

Lausanne
Angabe auf der Website des Arbeitgebers

Interspiritueller Raum (Géode) der Aumônerie auf dem Campus, ausdrücklich zum Beten und allen offen — nicht muslimspezifisch, Beschäftigte nicht genannt

Beleg hinterlegt — Zitat und Quelle siehst du mit kostenlosem Konto.

Kostenlos anmelden und Beleg ansehen

Selbstauskunft des Arbeitgebers, keine Zusage für diese Stelle — sprich es im Gespräch an.

Unternehmensprofil ansehen
Quelle

Diese Stellenanzeige stammt direkt von der Karriereseite des Arbeitgebers und wird dort gehostet.

Originalanzeige ansehen
Ich bin interessiert