Simon Geisler, Developer in Munich, Bavaria, Germany
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Simon Geisler

Verified Expert  in Engineering

Computer Vision Developer

Munich, Bavaria, Germany
Toptal Member Since
March 19, 2020

Simon is an AI and data science professional with more than five years of experience in data science, data engineering, software engineering, and system architecture design in the public cloud (products scaled to 10+ million users). In Germany and Silicon Valley, he worked on spatio-temporal IoT, natural language processing, and computer vision projects. Besides his strong affinity to modeling algorithms, Simon confidently develops production-grade microservices, Spark, and Kafka Streams apps.


Robert Bosch GmbH
Deep Learning, Artificial Intelligence (AI), Machine Learning, Algorithms...
Bosch Center for Artificial Intelligence (BCAI)
GPU Computing, Algorithms, Artificial Intelligence (AI), Machine Learning...
Robert Bosch GmbH
Data-driven Decision-making, Decision Modeling, Time Series...




Preferred Environment

Google Cloud, Azure, Continuous Delivery (CD), Continuous Integration (CI), Git, NVIDIA CUDA, C++, Numba, NumPy, Matplotlib, Pandas, JavaScript, Docker, Spark, Apache Kafka, Scikit-learn, TensorFlow, PyTorch, Scala, Python

The most amazing...

...project I've tech-led is an automotive, ML/data-heavy Kafka Streams cloud service, right from the beginning until we had more than 10 million users.

Work Experience

Senior System Architect

2019 - 2020
Robert Bosch GmbH
  • Reworked the system design for the road condition service (automated driving) on Azure using the latest services.
  • Designed and implemented data analytics and machine learning pipelines using Spark, Airflow, and Azure Data Explorer.
  • Improved scalability of the wrong-way driver warning in regions of 100+ million users.
  • Set up the development process from writing tickets until deployment.
Technologies: Deep Learning, Artificial Intelligence (AI), Machine Learning, Algorithms, Autonomous Navigation, Autonomous Robots, Automotive, Robotics, Apache Kafka, Kafka Streams, Grafana, Splunk, Monitoring, Continuous Delivery (CD), Continuous Integration (CI), GitLab CI/CD, GitLab, Git, Docker, Numba, NumPy, Pandas, Azure Machine Learning, Terraform, Azure Cache, Azure Event Hubs, Azure API Management, Azure Kubernetes Service (AKS), Azure Virtual Machines, Azure Virtual Networks, Azure SQL Databases, Azure Blobs, Azure Data Factory, Databricks, Apache Spark, PyTorch, Scikit-learn, TensorFlow, Python 3, Python, Scala

Deep Learning Intern

2019 - 2019
Bosch Center for Artificial Intelligence (BCAI)
  • Exceeded state of the art performance with multiple approaches for the task of hazard detection (one of the long tail tasks for automated driving).
  • Engineered custom deep learning architecture incorporating for real-time (embedded) object detection using stereoscopic vision.
  • Prepared cutting-edge research publications currently in review.
Technologies: GPU Computing, Algorithms, Artificial Intelligence (AI), Machine Learning, Autonomous Navigation, Autonomous Robots, Automotive, Robotics, Embedded Hardware, Stereoscopic Video, Object Detection, Semantic Segmentation, Image Classification, Computer Vision, Deep Learning, TensorFlow, PyTorch, Torchvision, Python 3, Python

Data Scientist | AI Engineer

2015 - 2019
Robert Bosch GmbH
  • Developed a detection algorithm for tracking vehicles based on GPS and Open Street Map based on particle filtering.
  • Designed and implemented a Kafka Streams architecture in Scala for out of order handling and scaling the detection algorithm (improved scalability to 100+ million users).
  • Implemented CI/CD pipelines in GitLab for building the project, executed tests of different granularities and license/dependency/security scanning, and dockerized deployment on Kubernetes.
  • Implemented various microservices with Node.js, namely for persisting data in the Azure Data Explorer (proprietary big data telemetry storage).
  • Developed various features and made production-ready on an Angular-based front end.
Technologies: Data-driven Decision-making, Decision Modeling, Time Series, Simultaneous Localization & Mapping (SLAM), Computer Vision, Data Pipelines, Data Integration, Deep Learning, Machine Learning, Artificial Intelligence (AI), Autonomous Navigation, Autonomous Robots, Automotive Controller Area Network (CAN), Robotics, Automotive, Azure Machine Learning, Shapely, YAML, XML, JSON, Yarn, Apache Storm, Hadoop, PyTorch, TensorFlow, Scikit-learn, NumPy, Numba, Pandas, Active Learning, Unsupervised Learning, Supervised Learning, Clustering, Anomaly Detection, Regression, Classification, AMQP, HTTP, Web API, Docker, Git, Bitbucket, Jira, Scrum, Azure DevOps Services, DevOps, Monitoring, Splunk, Terraform, Internet of Things (IoT), Databricks, Azure SQL Databases, Azure Data Factory, Azure Event Hubs, Azure API Management, Azure Kubernetes Service (AKS), Azure Cache, JavaScript, Node.js, Angular, Apache Spark, Spark ML, Spark SQL, Spark, Geospatial Analytics, Geospatial Data, PostGIS, PostgreSQL, OpenStreetMap, Apache Airflow, Apache Kafka, Kafka Streams, Python 3, Python, Scala, Java

Research and Development Intern

2012 - 2015
Robert Bosch GmbH
  • Supported several projects for Advanced Driver Assistance Systems (ADAS) focusing on digital maps and electronic horizon.
  • Organized and supervised user experience surveys and test group studies.
  • Developed an Android app for rapid prototyping of HMIs for a highway assistant that communicates via a Bluetooth CAN interface.
  • Developed an Android app (end-to-end) for hazard spots on the road, including the cloud service.
Technologies: Autonomous Navigation, Autonomous Robots, Robotics, Automotive, Docker, Kubernetes, Azure, Cloud Services, Web API, Android, C++, Scala, Java, Python, MATLAB

Cloud-based Wrong-way Driver Warning
A cloud service that detects wrong-way drivers, which had more than 10 million connected devices when I left.
I contributed to this project in various ways. Most importantly, I developed a particle filter-based detection algorithm for probabilistically spotting this needle-in-a-haystack phenomenon. Then as the technical lead, I led a team of two other data scientists on spatiotemporal data. I developed various big data pipelines with Apache Spark to crunch gigabytes of graph data. To scale the service, I led the development of a Kafka Streams architecture implemented in Scala.
In the DevOps process, it was also essential to develop further artifacts such as CI/CD pipelines for the Kubernetes-based architecture.

Stereo Vision Hazard Detection

Stereo vision hazard detection based on deep learning sensor data fusion architecture to sense any kind of hazardous objects in the drivable space for automated cars.
I delivered PyTorch and Tensorflow implementation of various architectures. I could exceed the current state of the art in a relative performance gain of more than 10%.
2020 - 2023

Ph.D. Degree in Computer Science

Technical University of Munich - Munich, Germany

2016 - 2019

Master of Science Degree in Data Science

Albstadt-Sigmaringen University and University of Mannheim - Albstadt, Mannheim, and Tübingen in Germany

2012 - 2015

Bachelor of Engineering Degree in Mechatronics and Micro Systems Engineering

Heilbronn University - Heilbronn, Germany


Software Architecture

oose Innovative Informatik eG


Software Quality

Robert Bosch GmbH (internal training)


Deep Learning Specialization


Developer Training for Spark and Hadoop



Statistical Learning (with Distinction)

Stanford University


Basic Certificate in Project Management (GPM)

GPM Deutsche Gesellschaft für Projektmanagement (German Society for Project Management)


Matplotlib, Scikit-learn, PyTorch, Pandas, TensorFlow, OpenCV, NumPy, Web API, Azure API Management, Spark ML, Node.js, AMQP, Shapely


Jupyter, GitLab CI/CD, Kafka Streams, Git, MATLAB, Apache Storm, Azure Kubernetes Service (AKS), Terraform, Azure Machine Learning, GitLab, Splunk, Grafana, Apache Airflow, Spark SQL, Azure DevOps Services, Jira, Bitbucket


Spark, Apache Spark, Hadoop, Yarn, Angular


Python 3, Python, SQL, OWL, Java, Scala, JavaScript, C++, XML, YAML


Data Science, Lambda Architecture, Extreme Programming, Spatial Databases, Automation, Continuous Integration (CI), Continuous Delivery (CD), Web Architecture, Anomaly Detection, DevOps, Scrum, Normalizing Flows


Apache Kafka, Jupyter Notebook, Azure, NVIDIA CUDA, Docker, Android, Kubernetes, Databricks, Azure Event Hubs


Databases, PostgreSQL, MongoDB, Redis, Data Pipelines, Data Integration, Google Cloud, SQL Architecture, Azure Blobs, Azure SQL Databases, Azure Cache, PostGIS, JSON

Industry Expertise

Automotive, Project Management


Agile Deployment, Machine Learning, Deep Learning, Data Mining, Data Analysis, Data Analytics, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Statistical Learning, Predictive Modeling, Torch, Big Data, Big Data Architecture, Algorithms, Image Classification, Semantic Segmentation, Object Detection, Stereoscopic Video, Microsoft Azure, Computer Vision, Recommendation Systems, Natural Language Processing (NLP), Autonomous Robots, Semantic Web, Time Series, Spatial Analysis, Localization, Simultaneous Localization & Mapping (SLAM), Quantitative Modeling, Geospatial Data, Geospatial Analytics, OpenStreetMap, Data Engineering, Generative Pre-trained Transformers (GPT), Web Mining, Programming, Knowledge Bases, Ontologies, Scientific Computing, Engineering, Signal Processing, Digital Signal Processing, Statistics, Bayesian Statistics, Numba, Statistical Methods, Naive Bayes, Decision Trees, Decision Analysis, Decision Modeling, Statistical Modeling, Multivariate Statistical Modeling, Natural Language Understanding (NLU), Software Design, Software QA, Software Architecture, System Design, High Code Quality, Quality Management, Distributed Systems, Data Architecture, Supervised Learning, Classification, Text Classification, Sentiment Analysis, Unsupervised Learning, Regression, Torchvision, Embedded Hardware, Cloud Services, Azure Data Factory, Azure Virtual Networks, Azure Virtual Machines, Monitoring, Internet of Things (IoT), HTTP, Clustering, Active Learning, Robotics, Autonomous Navigation, Automotive Controller Area Network (CAN), GPU Computing, Data-driven Decision-making, Controls, Deep Reinforcement Learning, Graphs, Graph Neural Networks, Transformers, Adversarial Robustness, Government Performance Management (GPM)

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