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) 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.
Why Prosus?
At Prosus, your colleagues include 60,000 AI agents… and your job is to figure out what that actually means. We're one of the largest operators of agentic AI on the planet, inside a $100B+ portfolio across 100+ companies and two billion people. The humans here own what AI can't do alone: judgment, creativity, and the call on what gets built next.
Own the impact. Work the ecosystem. Earn your growth. Build with backing.
Curious about our perks and benefits? Take a look at this page.
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.
Want to know more about life at Prosus? Explore our culture and values.
Skills Required
- 7+ years of machine learning engineering experience
- Technical leadership experience, including mentoring engineers, conducting code reviews, making architecture decisions, and delivering projects with measurable business impact
- Experience training and deploying language models to production, including pre-training, continued pre-training, or fine-tuning with rigorous evaluation and inference optimization
- Experience preparing large-scale training datasets through filtering, quality assessment, deduplication, and data mixture design
- Hands-on experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Axolotl
- Experience orchestrating multi-node training jobs, debugging failures, and optimizing throughput
- Strong understanding of training dynamics at scale, including loss stability, learning-rate schedules, and long-running distributed jobs
- Expert Python and PyTorch skills with production experience using Transformers, DeepSpeed, and Accelerate
- Published research at ML conferences, released models on Hugging Face, created public benchmarks, or contributed to open-source projects
- Experience with post-training methods such as RLHF, DPO, GRPO, or other reinforcement-learning approaches
- Experience optimizing production inference through quantization, model compression, distillation, vLLM, or TensorRT-LLM
- Understanding of memory optimization techniques including gradient checkpointing, mixed-precision training, and ZeRO optimization
- Deep knowledge of A100, H100, and H200 GPU architectures and their training and inference implications
- Track record of building synthetic-data generation pipelines for instruction tuning or domain adaptation
What We Do
Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. The group is focused on building meaningful businesses in the online classifieds, food delivery, payments and fintech, and education technology sectors in markets including India and Brazil. Through its Ventures team, Prosus invests in areas including health, logistics, blockchain, and social commerce. Prosus actively seeks new opportunities to partner with exceptional entrepreneurs who are using technology to improve people’s everyday lives. Every day, billions of customers use the products and services of companies that Prosus has invested in, acquired or built. Hundreds of millions of people have made the platforms of Prosus’s associates a part of their daily lives. For listed companies where we have an interest, please see: Tencent, Delivery Hero, Remitly, Trip.com, Udemy, Skillsoft, Sinch and SimilarWeb. Today, Prosus companies and associates help improve the lives of more than two billion people around the world.
