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 for
We're seeking a Senior Machine Learning Engineer to train domain-specific language models and provide technical leadership to the team. You'll own critical parts of our training infrastructure, mentor engineers, and drive technical decisions from data preparation through production deployment. You have deep hands-on experience training language models at scale, lead by example through rigorous experimentation and high-quality code, and are motivated by seeing your work deployed to millions of users. You thrive in fast-paced environments where you balance technical depth with practical business impact.
What you’ll do
Analyze model performance and training data, formulate hypotheses, design and execute rigorous experiments to systematically improve model quality, training and inference efficiency, and downstream task performance
Drive technical decision-making for model architecture, training strategies, and infrastructure choices
Provide technical leadership and mentorship to ML engineers and interns, conducting code reviews, sharing best practices, and accelerating team growth
Train large language models through continued pre-training and full parameter fine-tuning on proprietary datasets
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 and inference throughput (quantization, distillation, serving optimization), and monitor model performance throughout long-running distributed jobs
Build robust evaluation frameworks and establish metrics to measure model quality and guide decisions
Write production-grade, well-tested code and set engineering standards for the team
Minimum qualifications
7+ years of ML engineering experience
Technical leadership experience: mentoring engineers, conducting code reviews, making architecture decisions, and delivering projects with measurable business impact
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 and inference optimization
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)
Preferred qualifications
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 including quantization, model compression, distillation, and serving frameworks (vLLM, TensorRT-LLM)
Understanding of memory optimization: gradient checkpointing, mixed precision training (FP16, BF16, FP8), ZeRO optimization
Deep knowledge of GPU architectures (A100, H100, H200) and their implications for training and inference optimization
Track record of building synthetic data generation pipelines for instruction tuning or domain adaptation
What we offer
High-impact AI projects that are strategically vital to the company, with direct engagement from senior leadership including the CEO
State-of-the-art infrastructure: H200 GPU fleet, massive proprietary datasets, access to frontier models (OpenAI, Anthropic, Google, Together.ai) for evaluation and baselines
Expert colleagues who have released top Hugging Face models, authored papers at NeurIPS, created well-known benchmarks, and built multiple production AI systems
Significant autonomy and freedom to test ideas, experiment with new approaches, and drive technical decisions
Modern tooling: Latest ML frameworks, coding assistants, best-in-class development environment
Hybrid 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 learning
If you're excited to apply your LLM training expertise to high-impact applications at scale, lead technical initiatives, and grow the next generation of ML engineers, let's talk.
