Werde Teil unseres Teams
Baue souveräne KI für kritische Infrastruktur
Forschungsmentoring für MSc & PhD Studierende
Nimm an unserem Mentoring-Programm für generative KI-Forschung teil. Arbeite an kleinen Sprachmodellen, mechanistischer Interpretierbarkeit und domänenspezifischen ML-Themen mit Compute-Zugang und praktischer Betreuung.
The Program
A hands-on mentorship program for MSc and PhD students interested in generative AI research. You’ll work on real problems — small language models, mechanistic interpretability, and domain-specific ML — with compute access and direct guidance from our research team.
What You’ll Work On
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Small language model training and evaluation
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Mechanistic interpretability experiments
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Domain-specific fine-tuning for energy applications
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Agent architecture design and benchmarking
What You Bring
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Active MSc or PhD student in ML, NLP, or related field
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Familiarity with PyTorch and transformer architectures
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Strong motivation to publish and ship research
What We Provide
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Compute access for experiments
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Weekly 1:1 mentorship sessions
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Co-authorship on publications
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Potential path to full-time role
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ML Engineer
Verantworte Training- und Inferenz-Infrastruktur für 1B+ Parameter Modelle. Baue verteilte Training-Pipelines mit FSDP, DeepSpeed und arbeite direkt mit der Forschung, um Architektur-Ideen in Experimente umzusetzen.
The Role
An ML engineer who has trained and served language models before. You’ll own training and inference infrastructure — from setup to distributed training and inference.
What You’ll Do
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Train/post-train and iterate on 1B+ parameter models across multi-GPUs
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Build and optimize distributed training and inference infrastructure (FSDP, DeepSpeed, llm-d)
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Work directly with research to turn architecture ideas into running experiments
What You Bring
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Hands-on experience training language models
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Strong PyTorch; familiarity with distributed training frameworks
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Comfortable with Linux, cluster management
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Background in HPC or cloud infrastructure (Local, AWS, GCP)
Nice to Have
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Experience with MoE architectures and sparse models
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Contributions to open-source ML training tools
Why EnergyAI
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Shares in the company, competitive salary
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Access to local and cloud compute
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Experiments with direct business impact
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AI startup building sovereign AI for critical infrastructure
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