Senior Machine Learning Engineer - AI Integration Engineer
Senior ML engineer builds and deploys computer-vision AI models for smartphone-based 3D motion analysis used in clinical diagnostics and orthopedics.
- Compensation
- Not disclosed
- Location
- Berlin, Germany
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- A degree (BSc or MSc) in Computer Science, Mathematics/Statistics, Engineering, or a related field.
- 3+ years of experience in AI, deep learning, or computer vision, with a focus on real-world model integration.
- Good knowledge of AI/ML solutions, algorithms, and best practices, with the ability to apply them to solve complex problems.
- Experience working with large heterogeneous datasets, with a strong ability to manipulate, analyze, and derive insights from data.
- Proficiency in Python, with hands-on experience using deep learning frameworks like PyTorch and inference platforms like ONNX and TensorRT.
- Familiarity with app & software development (Kotlin, Swift and Rust) is a plus.
- Familarity with MLOps (DVS) and DevOps Engineering tools, like Docker, Kubernetes and CI/CD pipeline like Gitlab runners.
- A collaborative mindset, excellent problem-solving skills, and the ability to take initiative.
- Fluent in English; German skills are a plus.
- Willingness to work on-site in Berlin 3 days a week.
Full description
LINDERA is an AI-driven medical device manufacturer founded in 2017, specializing in Computer Vision-based movement analysis for clinical and healthcare applications.
The company addresses a fundamental challenge in modern healthcare: the need for objective, scalable, and accessible movement diagnostics. Traditional gait and movement analysis requires expensive laboratory equipment, specialized facilities, and expert personnel—creating barriers to widespread clinical adoption and limiting access for patients who would benefit most.
LINDERA transforms movement analysis through smartphone-based 3D motion tracking technology. Using proprietary AI algorithms, the company's platform analyzes gait parameters and movement patterns through standard smartphone cameras, eliminating the need for specialized hardware while maintaining clinical-grade accuracy. This democratization of movement diagnostics enables healthcare providers across multiple specialties to integrate objective biomechanical data into clinical decision-making.
Core Technology
LINDERA's patented Computer Vision AI analyzes video recordings to extract precise 3D skeletal motion data, capturing eight key gait parameters and biomechanical markers. The technology operates on-device for data privacy, functions offline, and delivers results comparable to gold-standard laboratory systems (validated against GAITRite).
Market Applications
The platform serves diverse healthcare segments:
Medical Supply & Orthopedics: Objective gait analysis for assistive device prescriptions and orthotic recommendations in medical supply stores (Sanitätshäuser)
Clinical Diagnostics: Movement analysis for neurological conditions (Parkinson's, MS, stroke rehabilitation), pediatric movement disorders, and post-surgical monitoring
Care Assessment: AI-supported evaluation tools for care level determination and mobility tracking in long-term care settings
Preventive Healthcare: Risk assessment and intervention planning for mobility-related health concerns across age groups
Regulatory & Validation
LINDERA operates as a certified medical device manufacturer under MDR (Medical Device Regulation) standards, with ISO 13485 and ISO 27001 certifications. The technology is scientifically validated through peer-reviewed research and clinical partnerships with leading healthcare institutions.
Strategic Positioning
LINDERA stands at the intersection of digital health innovation and practical clinical application. By making advanced movement analysis accessible through ubiquitous smartphone technology, the company enables healthcare providers to integrate objective biomechanical data into diagnosis, treatment planning, and outcome monitoring—addressing unmet medical needs while creating a scalable platform for multiple healthcare applications.