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StratifAI · 2 months ago

Machine Learning Engineer (m/w/d)

Hybrid

Full-time, Contract

Hybrid

Mid, Senior Level

StratifAI is an innovative precision oncology startup developing AI-based cancer biomarkers. As a Machine Learning Engineer, you will advance scientific research and product development by developing AI models and scalable deployment pipelines, while exploring emerging technologies in biomedical AI.

Artificial Intelligence (AI)Health CareOncology

Responsibilities

Develop, train, and deploy deep learning models for histopathological image analysis (e.g., classification, segmentation, instance detection)

Optimize ML models for performance and scalability in production

Work closely with cloud engineers to ensure seamless deployment on cloud infrastructure

Collaborate with quality managers to translate R&D models into production-ready, regulatory-compliant tools

Write and maintain technical documentation for machine learning workflows and systems

Stay up to date with the latest research in computer vision, deep learning, and biomedical AI

Qualification

PythonDeep Learning frameworksMachine learning workflowsComputer visionHealthcare dataCloud platformsProblem-solvingCommunication skillsPresentation skillsCollaborative work

Required

Strong proficiency in Python and experience with Deep Learning frameworks (e.g., PyTorch)

Expertise in machine learning workflows, including data preprocessing and model optimization, as well as agentic AI systems

Hands-on experience working with state-of-the-art computer vision and deep learning models, such as CNNs and Transformers

Experience working with healthcare data such as histopathology and radiology scans

Master's or PhD in Computer Science, Machine Learning, Computational Pathology, or related field

Ability to work collaboratively in cross-functional teams, including scientists, statisticians, and regulatory experts

Strong problem-solving, communication, and presentation skills

Preferred

Experience working in regulated software development lifecycles (IEC 62304, ISO 13485)

Background in digital pathology

Familiarity with cloud platforms like AWS for scalable model training and deployment

Startup or high-growth team experience

Familiarity with tools like GitHub Copilot, Cursor, Replit, or other AI productivity tools

Solid engineering practices: version control, testing, containerization

Benefits

Competitive salary, stock options, and extra benefits.

A beautiful office near Alexanderplatz with an on-site gym, private showers, free drinks and snacks, rooftop bar, co-working space, and a quiet room for meditation or prayer.