Machine Learning Engineer – LLMs

Join our AI team at Prosus, the largest consumer internet company in Europe and one of the biggest tech investors in the world. You'll be working on the team that drives growth and innovation across the company, with your work directly impacting how millions of people shop online.Who We’re Looking ForWe'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.What You’ll DoAnalyze model performance and training data to identify improvement opportunities and formulate hypotheses for enhancementDesign, execute, and rigorously evaluate experiments to improve model quality, training efficiency, and downstream task performanceTrain large language models for our domain through continued pre-training and full parameter fine-tuning on proprietary datasetsDesign and implement post-training pipelines using RLHF, DPO, GRPO, and other reinforcement learning techniquesBuild and optimize distributed training infrastructure across multi-node GPU clusters using frameworks like DeepSpeed, FSDP, Megatron-LM, or AxolotlOwn large-scale data preparation: filtering, quality assessment, deduplication, and data mixture strategies for training corpora at 100B+ token scaleGenerate and curate high-quality synthetic data for instruction fine-tuning and capability enhancementDebug training stability issues, optimize training throughput, and monitor model performance throughout long-running distributed jobsImplement techniques from recent papers and adapt them to our use casesBuild robust evaluation frameworks to measure model quality and guide training decisionsWrite production-grade, well-tested code and contribute to team engineering standardsMinimum Qualifications5+ years of ML engineering experienceProven 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 evaluationExperience preparing large-scale training datasets: data filtering, quality assessment, deduplication strategies, and data mixture designHands-on experience with distributed training frameworks (DeepSpeed, FSDP, Megatron-LM, or Axolotl) including orchestrating multi-node jobs, debugging failures, and optimizing throughputStrong understanding of training dynamics at scale: debugging loss instabilities, tuning learning rate schedules, managing training stability across long-running multi-node jobsExpert Python and PyTorch with production experience using training libraries (Transformers, DeepSpeed, Accelerate)Strong software engineering practices: version control, testing, CI/CD, code reviewPreferred QualificationsPublished research at ML conferences (NeurIPS, ICML, ICLR, ACL, EMNLP), released models on Hugging Face, created public benchmarks, or contributed to open-source projectsExperience with post-training methods: RLHF, DPO, GRPO, or other reinforcement learning approaches for alignment and instruction-followingExperience 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 optimizationTrack record of building synthetic data generation pipelines for instruction tuning or domain adaptationResearch contributions: published papers at ML conferences, released models on Hugging Face, or created benchmarks used by the communityContributions to open-source LLM training projects or toolsWhat We OfferHigh-impact AI projects that are strategically vital to the company, with direct engagement from senior leadership including the CEOState-of-the-art infrastructure: H200 GPU fleet, massive proprietary datasets, access to frontier models (OpenAI, Anthropic, Google, Together.ai ) for evaluation and baselinesExpert colleagues who have released top Hugging Face models, authored papers at NeurIPS, created well-known benchmarks, and built multiple production AI systemsSignificant autonomy and freedom to test ideas, experiment with new approaches, and drive technical decisionsModern tooling: Latest ML frameworks, coding assistants, best-in-class development environmentHybrid work model with our Amsterdam office - home to the AI House, bringing together 200+ AI professionals through events, meetups, and startup collaborations Competitive compensation, top-spec MacBook Pro, and an environment genuinely built for professional growth and learningIf you're excited to apply your LLM training expertise to high-impact applications at scale and see your work make a tangible difference globally, let's talk.Our Diversity & Inclusion Commitment We respect the dignity and human rights of individuals and communities wherever we operate in the world. Building an inclusive workplace where everyone feels welcome and can thrive is critical for us. We provide access to education, which helps everyone understand the important role they play and the positive impact they can have.For a deeper look at our journey and future plans, explore our latest Annual Report . Stay up to date with our latest news to see what makes Prosus stand out. Learn more at www.prosus.com .