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Avelios Medical

Neuromorphic processors for brain-like sensing

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMunich, GermanyNo salary listedIn-person

About the Job

Requirements

  • A degree in Informatics, Computer Science, or a related field, or equivalent practical experience.
  • A strong coding background; GitHub contributions are a plus.
  • Hands-on experience deploying machine-learning systems to production using PyTorch, LangChain, and Pydantic, including large language models, automatic speech recognition models, and retrieval-augmented generation pipelines with vector databases such as Qdrant.
  • Fluency in English.

Responsibilities

  • Collaborate with the core development team to bring machine learning into production.
  • Design and train machine-learning models on large-scale clinical datasets.
  • Integrate AI-powered solutions, including large language models, automatic speech recognition, retrieval-augmented generation, and embedding, into real medical workflows.
  • Evaluate, benchmark, and fine-tune existing models for clinical use cases.
  • Work with federated machine-learning frameworks for privacy-preserving model training.
  • Collaborate with academic and research institutions.
  • Own the reliability, performance, and quality of AI features in production.
  • Use high-performance computing infrastructure to build robust machine-learning pipelines.

Desired Qualifications

  • Publications at top conferences or demonstrated cutting-edge results in applied machine learning, such as speech recognition or natural language processing.
  • German language ability.

About the Company

Avelios Medical

Neuromorphic processors for brain-like sensing

Avelios Medical makes ultra-efficient neuromorphic processors that imitate how the brain processes information. Their devices use analog-mixed signal circuits to recreate spiking neurons and synapses, allowing sensor data to be processed faster and with much less energy than traditional computer chips. The hardware-based approach is different from software-focused AI by building brain-like processing directly into the processor. This lets businesses access and analyze sensor data more quickly while consuming less power, with goals that include enabling AI-powered healthcare tools and other healthcare-digital workflows.

Company size
201-500
Company stage
Series A
Total funding
$37.7M
Headquarters
Munich, Germany
Founded
2020

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