Machine Learning Engineer

  • $65,000.00 - $90,000.00/year
  • Statice is a Berlin-based company, enabling privacy-preserving data sharing and analysis, using anonymization and synthesization methods based on the latest research and technologies.

    Learn more about what we do:

    You will be part of our team, working on one the of the most promising data science challenges. At Statice, you'll be contributing to the core of our product and be responsible for the Machine Learning part of the product.

    You will:

    • Oversee decisions regarding the ML components in the product
    • Work and collaborate within a team of experienced people with diverse backgrounds
    • Research and implement the synthesization algorithms
    • Shape and contribute to our production-ready solution
    • Keep up with the latest research on machine learning with a focus on privacy, deep learning, and generative models.
    • Continuously share your experience with your colleagues

    Your profile:

    • Extensive experience in statistical analysis and machine learning.
    • Advanced knowledge in deep learning: from the theory to the popular frameworks.
    • Extensive experience with Python and its open-source data science ecosystem (numpy, scipy, pandas, scikit-learn, tensorflow, pytorch, etc.).
    • Experience with generative models

    Nice to have:

    • Experience with production grade machine learning
    • Experience with Bayesian statistics and probabilistic programming
    • Technical knowledge of data privacy techniques

    Our team:

    We are a small and diverse team with a big vision. Solving one of the most challenging data science issues takes a special mindset and motivation. This is why we value everybody’s contribution and strive for collaborative decisions that are founded in facts and rational arguments. We value openness and honesty towards each other and always exchange direct and constructive feedback.

    Compensation and perks:

    • Competitive salary and company stock options
    • Learning budget
    • Flexible working hours and home office policy
    • Participation in conferences and events: both as a speaker and as an attendee

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