Research Scientist (AI)

TL;DR

Research Scientist (AI): Designing, implementing, and deploying cutting-edge research in reinforcement learning and post-training at scale, focusing on driving innovations that make it into production. Focus on post-training large models to align them with human intent and enable general capabilities such as reasoning, pushing the boundaries of model performance, safety, and efficiency.

Location: Hybrid work schedule, with team members coming into the office twice a week in Berlin, Cologne, Hamburg, Munich, or London.

Company

hirify.global is a global communications platform powered by Language AI, offering human-sounding translations and intelligent writing suggestions designed with enterprise security in mind.

What you will do

• Build and deploy state-of-the-art reinforcement learning pipelines at scale.
• Post-train large (multi-modal) models to align them with human intent and enable general capabilities such as reasoning, pushing the boundaries of model performance, safety, and efficiency.
• Keep the entire lifecycle of research and production in mind: from idea conception, theoretical modeling, prototyping, ablation studies, all the way to production deployment.
• Build and foster external collaborations with academic and industrial partners.
• Follow scientific and technical standards for experimentation, reproducibility, and model evaluation.
• Collaborate hirify.globaly with Engineering, ML Platform, and HPC teams to deliver robust and reliable model updates to users.

Requirements

• A solid mathematical background and enjoy solving challenging problems, evidenced by a masters degree, diploma, PhD, or equivalent industry experience in mathematics, physics, computer science, or a related field.
• Deep practical experience in Python and at least one modern machine learning framework such as PyTorch, TensorFlow, or JAX, experience working with large compute clusters and ML infrastructure is a plus.
• A track record of leading self-directed research projects that go well beyond academic exercises and deliver tangible results.
• Expertise in deep reinforcement learning (RLHF/RLAIF/RLVR) is a plus.
• Hands-on experience scaling and deploying LLMs or other foundation models in real-world systems is a plus.

Culture & Benefits

• Diverse and internationally distributed team with people of more than 90 nationalities.
• Open communication, regular feedback, smooth collaboration, direct and actionable feedback, and believe that leading with empathy and growth mindset makes us better together.
• Hybrid work schedule, with team members coming into the office twice a week.
• Flexible working hours.
• 30 days of annual leave (excluding public holidays) and access to mental health resources.
• Virtual Shares.