We'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 DO
Analyze model performance and training data to identify improvement opportunities and formulate hypotheses for enhancement
Design, execute, and rigorously evaluate experiments to improve model quality, training efficiency, and downstream task performance
Train large language models for our domain through continued pre-training and full parameter fine-tuning on proprietary datasets
Design and implement post-training pipelines using RLHF, DPO, GRPO, and other reinforcement learning techniques
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 throughput, and monitor model performance throughout long-running distributed jobs
Implement techniques from recent papers and adapt them to our use cases
Build robust evaluation frameworks to measure model quality and guide training decisions
Write production-grade, well-tested code and contribute to team engineering standards
MINIMUM QUALIFICATIONS
3+ years of ML engineering experience
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
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)
Strong software engineering practices: version control, testing, CI/CD, code review
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: quantization, model compression, distillation, and serving frameworks (vLLM)
Understanding of memory optimization: gradient checkpointing, mixed precision training (FP16, BF16, FP8), ZeRO optimization
Track record of building synthetic data generation pipelines for instruction tuning or domain adaptation
Research contributions: published papers at ML conferences, released models on Hugging Face, or created benchmarks used by the community
Contributions to open-source LLM training projects or tools
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, 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 and see your work make a tangible difference globally, 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 https://prosusbenefits.com/.
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 https://www.prosus.com/the-prosus-way.
