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# Machine Learning Engineer - LLMs

- **Company:** Prosus
- **Location:** Amsterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Training Data, Big Data, Computer Clusters, Software Code Optimization, Code Review, Continuous Integration, Data Deduplication, Software Debugging, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Model Compression, Reinforcement Learning, Data Processing, Pytorch, DeepSpeed, Large Language Models, Discretization, HuggingFace, Megatron, Software Version Control
- **Published:** October 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=15e71ee7cbae9ce3

## About the Role

We're seeking an experienced Machine Learning Engineer to develop our domain-specific language models. You'll own the full training pipeline - from continued pre-training on proprietary data, through fine-tuning, to advanced post-training techniques. You have hands-on experience training language models at scale (from embedding models to specialized LLMs), deep understanding of distributed systems and excel at turning research concepts into robust production systems. You're motivated by seeing your work deployed and used by millions of real users. You thrive in fast-paced environments where you balance research insights with practical business impact., * 3+ years of ML engineering experience

 * Proven experience training and deploying language models to production (embedding models, encoder models, or large language models) including pre-training, continued pre-training, or fine-tuning with rigorous evaluation
 * Experience preparing large-scale training datasets: data filtering, quality assessment, deduplication strategies, and data mixture design
 * Hands-on experience with distributed training frameworks (DeepSpeed, FSDP, Megatron-LM, or Axolotl) including orchestrating multi-node jobs, debugging failures, and optimizing throughput
 * Strong understanding of training dynamics at scale: debugging loss instabilities, tuning learning rate schedules, managing training stability across long-running multi-node jobs
 * Expert Python and PyTorch with production experience using training libraries (Transformers, DeepSpeed, Accelerate)
 * Strong software engineering practices: version control, testing, CI/CD, code review, * Published research at ML conferences (NeurIPS, ICML, ICLR, ACL, EMNLP), released models on Hugging Face, created public benchmarks, or contributed to open-source projects
 * Experience with post-training methods: RLHF, DPO, GRPO, or other reinforcement learning approaches for alignment and instruction-following
 * Experience optimizing models for production inference: quantization, model compression, distillation, and serving frameworks (vLLM)
 * Understanding of memory optimization: gradient checkpointing, mixed precision training (FP16, BF16, FP8), ZeRO optimization
 * Track record of building synthetic data generation pipelines for instruction tuning or domain adaptation
 * Research contributions: published papers at ML conferences, released models on Hugging Face, or created benchmarks used by the community
 * Contributions to open-source LLM training projects or tools

## Description

* Analyze model performance and training data to identify improvement opportunities and formulate hypotheses for enhancement
 * Design, execute, and rigorously evaluate experiments to improve model quality, training efficiency, and downstream task performance
 * Train large language models for our domain through continued pre-training and full parameter fine-tuning on proprietary datasets
 * Design and implement post-training pipelines using RLHF, DPO, GRPO, and other reinforcement learning techniques
 * Build and optimize distributed training infrastructure across multi-node GPU clusters using frameworks like DeepSpeed, FSDP, Megatron-LM, or Axolotl
 * Own large-scale data preparation: filtering, quality assessment, deduplication, and data mixture strategies for training corpora at 100B+ token scale
 * Generate and curate high-quality synthetic data for instruction fine-tuning and capability enhancement
 * Debug training stability issues, optimize training throughput, and monitor model performance throughout long-running distributed jobs
 * Implement techniques from recent papers and adapt them to our use cases
 * Build robust evaluation frameworks to measure model quality and guide training decisions
 * Write production-grade, well-tested code and contribute to team engineering standards