Location:
Ludwigsburg
About Us
Deep Care GmbH is a fast-growing health-tech company reinventing occupational health and wellbeing. With our flagship products
Isa
and
Liv
, we combine cutting-edge hardware, biosensing, and AI-driven coaching to support millions of employees worldwide. We are a trusted partner of leading health insurers, corporate customers, and public institutions.
As part of our innovation pipeline, we are driving an advanced project, which is about applying state-of-the-art
language models (LLMs/multimodal-SLMs)
to create groundbreaking workplace health solutions.
We are now looking for an outstanding engineer who can join our team and push the boundaries of AI and applied software development.
Your Role
* Design, train, fine-tune, and deploy LLM/SLM-based solutions (including smaller language models for edge devices).
* Integrate AI pipelines into scalable, production-ready software .
* Collaborate with ergonomics, health, and design experts to make AI usable, explainable, and adaptive in real-world settings.
* Drive algorithmic innovation for coaching, personalization, and data interpretation.
* Contribute to the full software lifecycle: architecture, implementation, testing, optimization, and documentation.
Your Profile
* Proven expertise in Large/Small Language Models (LLMs/SLMs): fine-tuning, embeddings, multi-modal extensions, inference optimization.
* Superb programming skills (Python or C++; strong grasp of software architecture, clean code, testing frameworks).
* Solid knowledge of ML frameworks (PyTorch, TensorFlow, etc.).
* Familiarity with data privacy, GDPR/AI Act, and responsible AI principles.
* Strong problem-solving mindset, able to work in a multidisciplinary, fast-moving team.
* (Bonus) Experience with edge AI, health tech, ergonomics, or wearable systems.
What We Offer
* A central role in a high-impact European innovation project.
* Work at the cutting edge of AI for health & prevention.
* Join a diverse, international, highly motivated team.
* Fair compensation, growth opportunities, and public-funded project exposure.