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 estab