Senior Machine Learning Engineer – LLMs

Who We Are You've ordered dinner through Just Eat Takeaway. You've bought and sold on OLX. You've paid with PayU. Chances are, you've already used a Prosus company today. We are Prosus, a global technology company powering some of the world's leading lifestyle ecommerce businesses. Every day, billions of people across India, Latin America, Europe and beyond use our products and services to buy, sell, pay, connect and discover. Our AI-driven ecosystem spans 100+ companies across ecommerce, food delivery, fintech and experiences, serving billions of consumers worldwide. AI powers how we operate and innovate, with more than 60,000 active AI agents helping our teams build, scale and move faster. Through our investment team, we also back the founders and technologies shaping the future: From robotics and autonomous systems to synthetic biology and frontier AI. Guided by our five values Innovation, Results, People, Impact, and Entrepreneurship, culture is our competitive advantage. We thrive with experimentation; we build, we test, we learn, we iterate. Join us in shaping the future of technology at global scale. 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 http://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, a