Senior Machine Learning Engineer | up to €180k

Bluebird; Berlin; On-site; EUR 120,000 - 190,000; Full time

Job summary

Bluebird in Berlin is seeking an experienced ML engineer to join its in-house AI team. You will train and fine-tune language models, build scalable distributed training on multi-node GPU clusters, and own data preparation.

You will mentor engineers, influence architecture decisions, and collaborate with senior leadership as AI remains a top priority.

Qualifications

• 7+ years in ML engineering with production LLMs.
• Hands-on multi-node training experience.
• Expert in Python and PyTorch.
• Experience leading ML teams.
• Nice to have: RLHF, vLLM, distillation, NeurIPS/ICML/ACL/EMNLP papers, Hugging Face PRs.

Responsibilities

• Train LLMs with continued pre-training and full fine-tuning on large proprietary datasets.
• Build and optimise distributed training on multi-node H200 clusters (DeepSpeed, FSDP, Megatron-LM).
• Own data at scale: filtering, deduplication, quality and data mixtures for corpora of 100B+ tokens.
• Generate synthetic data for instruction tuning and build evaluation frameworks.
• Lead the technical work: architecture decisions, code reviews and mentoring ML engineers.

Skills

Language model training

Multi-node training

Python

PyTorch

Technical leadership

Tools

Transformers

DeepSpeed

Job description

This isn't another ML role where you're calling other models, you'll be actively training the language models that millions of people use when they shop online. This AI team does more than call APIs. It does continued pre-training on 100B+ tokens of its own data, fine-tunes models, and puts them into production on its own GPU cluster.

About the company: who will you join?

• One of Europe’s largest consumer internet groups, with marketplaces and e-commerce companies that serve millions of users


• An in-house AI team that trains its own domain-specific LLMs, with models already in production


• Full support from senior leadership, up to the CEO. AI is the company’s top priority


• A team that is growing fast, with plans to build its own foundation models and a large increase in GPU capacity

About the role: what will you do here?

• Train LLMs with continued pre-training and full fine-tuning on large proprietary datasets


• Build and optimise distributed training on multi-node H200 clusters (DeepSpeed, FSDP, Megatron-LM)


• Own data at scale: filtering, deduplication, quality and data mixtures for corpora of 100B+ tokens


• Generate synthetic data for instruction tuning, and build the evaluation frameworks that guide decisions


• Lead the technical work: make architecture decisions, review code and mentor ML engineers

About you: what experience do you bring?

• 7+ years in ML engineering. You have trained language models and put them into production yourself (LLMs, encoders or embedding models)


• Hands-on experience with multi-node training. You know how to fix loss spikes, NCCL timeouts and slow throughput


• Expert in Python and PyTorch (Transformers, Accelerate, DeepSpeed)


• Experience as a technical lead. You make others better and focus on results for the business


• Extra points for: DPO/GRPO/RLHF, vLLM and distillation, papers at NeurIPS/ICML/ACL/EMNLP, or models and PRs on Hugging Face