We are a seed-funded, high-tech company from Berlin reinventing the way machines interact with humans.

We combine the ability to create precise 3D head models on the spot with the latest facial & emotion recognition to spark the human element in the next generation of smart virtual assistants. Our technology is already being used in applications from virtual measurements & try-ons in e-commerce over safety & security cameras to the next generation of face-to-face customer interactions at scale.


Our two co-founders Kevin and Julian have more than 15 years of experience in mathematical research, technology start-ups and top-tier consulting.


Founded in 2018, we are small, high performing team of eight headquartered in Berlin, Germany, are funded for the next 18-24 months, we are a diverse tribe with Brazilian, Croatian, German, Italian, Macedonian, and Russian roots, and are now looking to grow our team and scale our customer base. For this, we are looking for exceptional individuals who share our passion and want to join us on our exciting journey.


We are looking for an experienced Machine Learning Engineer to take on a leading role and help developing the core technology of our business. Reporting to Chief Technical Officer (CTO), your primary tasks will be:

  • Assert that all production tasks are working properly in terms of actual execution and scheduling

  • Abuse machine learning libraries to their extremes, often adding new functionalities

  • Ensure that data science code is maintainable, scalable and debuggable

  • Automate and abstract away different repeatable routines that are present in most machine learning tasks

  • Bring the best software development practices to the data science team and helps them speed up their work

  • Choose best operational architecture together with devops team

  • Look constantly for performance improvement and decides which ML technologies will be used in production environment


You will also be required to provide input and interact with the frontend, backend, devops, QA, security and business stakeholders.

We encourage an open feedback culture in which all team members share and discuss feedback continuously. In addition, personal development goals for each team member will be defined jointly and reviewed regularly. For you, personal development goals will be defined and reviewed informally with the CTO on a monthly basis. Performance will be reviewed more formally on a quarterly basis by the CTO in conjunction with Board Members as is appropriate.



The following skills and experiences are fundamental to the job:

  • Computer science fundamentals and programming: This includes data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.). You must be able to apply, implement, adapt or address them (as appropriate) when programming. Practice problems, coding competitions and hackathons are a great way to hone your skills.

  • Probability and statistics: Ability of formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.). Profound skills in the field of statistics, such as application of various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.).

  • Data modelling and evaluation: You must explore and design the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.), continually evaluate how good a given model is, choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.), in addition, a deep understanding of measures to utilize resulting errors to tweak the models (e.g. backpropagation for neural networks) is a must

  • Applying machine learning algorithms and libraries: You will make use of standard implementations of machine learning algorithms extended through libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.) and applying them effectively choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), understanding how hyperparameters affect learning is a plus. You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.).

  • Software engineering and system design: Your typical output or deliverable is software (or a small component that fits into a larger ecosystem of products and services). You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality and maintainability and you have at least basic knowledge of these principles.


  • Competitive salary

  • Possibility of stock options (ESOP)

  • Working with a stellar team with 15+ years’ experience in university research, tech start-ups and top-tier consulting

  • Flexible working patterns

  • 25 days holiday

  • Subsidised healthcare protection

  • Free Urban Sports Club membership