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Post-Training Engineer - Apertus

emploi.epfl.ch -
Losanna, VD

Candidati adesso

Dettagli della posizione

Tempo determinato

Qualifiche

Dottorato
Conoscenza lingue straniere
Ricerca
Master
Bachelor
Reti informatiche
Capacità di comunicazione

Descrizione completa della posizione

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

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Voglio ricevere gli ultimi avvisi Job Alert per il ruolo di building information modeling nella seguente località: vaud

Introduction

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