Machine Learning Engineer (w/m/d)

About Us

StratifAI is an innovative Berlin-based precision oncology startup developing the next generation of AI-based cancer biomarkers. The founders have published over 100 articles in top-tier journals and founded StratifAI to translate these novel ideas into patient care.

Our products enable cancer patients to receive the right treatment at the right time – both for existing drugs on the market, and by supporting pharmaceutical companies in developing the drugs of tomorrow.

Job Description

What We Offer

• The opportunity to make a real difference for cancer patients.
• 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.
• Competing at the global top-tier of cancer research together with the founders, who are renowned researchers in the field of AI for biomarker development from medical imaging.
• An extremely fast-paced and high-growth VC-funded start-up environment with key ownership from day 1.

Our Core Values:

1. Trust, honesty & open communication between everyone
2. Saving time >>> money. Moving fast, 80/20 principle
3. Mistakes are ok as long as we learn from them, fast
4. We aim to be the best and deliver the highest quality, without egos

Job Key Responsibilities

• Develop, train, and deploy machine learning models for cancer biomarker detection and prediction.
• Develop scalable data pipelines to preprocess and manage large biomedical datasets.
• Optimize ML models for performance and scalability in production.
• Work closely with cloud engineers to ensure seamless deployment on cloud infrastructure.
• Conduct regular model evaluations to maintain performance and reliability over time.
• Write and maintain technical documentation for machine learning workflows and systems.
• Stay up to date with the latest advances in ML and implement relevant innovations in our platform.

Qualification & Skills

• Strong proficiency in Python and experience with Deep Learning frameworks such as PyTorch.
• Familiarity with cloud platforms like AWS for scalable model training and deployment.
• Expertise in machine learning workflows, including data preprocessing and model optimization.
• Solid understanding of deep learning architectures, including CNNs and Transformers.
• Experience with MLOps tools for model versioning, monitoring, and retraining pipelines.
• Strong problem-solving and communication skills.
• Experience working with state-of-the-art computational pathology models and LLM-agents.