11.10.2026 aktualisiert
100 % verfügbar
Software & Machine Learning Engineer (from ETHZ)
Geneva, Schweiz
Nur Remote
ETH Zurich (MSc Data Science) · Carnegie Mellon University · EPFL (BSc Computer Science)Über mich
Machine Learning Scientist with 5+ years of experience in ML and software. Specialized in Python, PyTorch, PySpark, forecasting, optimization, LLM/RAG and data pipelines. Passionate about AI and robotics, with experience turning ideas into production-ready solutions.
Skills
RAGA/B TestingAPIsApache AirflowBig DataContinuous DeliveryNachfrage-PrognoseDjangoPythonMachine LearningNatural Language ProcessingAWS CLIAzure Machine LearningSQLPyTorch
- Machine Learning & Statistical Modeling
5+ years of experience developing and deploying machine learning and statistical models in industry, including CatBoost, LightGBM, GLMs, neural networks, time-series forecasting, constrained non-linear optimization, and predictive modeling. Applications include pricing optimization, financial risk prediction, forecasting, and robotics.
- Python, ML Engineering & MLOps
Strong Python expertise for building production-ready ML systems and end-to-end pipelines. Experience with model packaging, APIs, Docker, GitLab CI/CD, Airflow, automated testing, model deployment, experiment tracking, and productionization of existing models.
- Big Data & Large-Scale Analytics
Experience analyzing and modeling datasets exceeding 1B rows using PySpark, Databricks, Trino, SQL, and distributed computing environments. Comfortable building large-scale analytical pipelines and transforming business problems into scalable data solutions.
- Cloud & ML Platforms
Hands-on experience with AWS, Azure, Databricks, Dataiku, RunPod, Hugging Face, and Weights & Biases for model development, training, deployment, monitoring, and experimentation.
- Deep Learning & Generative AI
Experience developing and training deep learning models with PyTorch, including Transformer-based architectures and custom AI policies. Familiar with Hugging Face ecosystems, Sentence Transformers, embeddings, LLM/RAG applications, and modern Vision-Language-Action models.
- Forecasting, Optimization & Decision Systems
Experience building forecasting and optimization systems for business applications, including pricing optimization, demand-related modeling, constrained optimization, elasticity estimation, and decision-support tools.
- A/B Testing & Experimentation
Design and analysis of controlled experiments using confidence intervals, hypothesis testing, p-values, statistical power analysis, and business/econometric signals such as price elasticity, conversion rates, and propensity scores.
- Data Engineering & Databases
Experience with PostgreSQL, MongoDB, Databricks, Trino, SQL, PySpark, and data pipelines for ingestion, transformation, analytics, and machine learning workloads.
- APIs & Full-Stack Development
Development of web applications, dashboards, and APIs using FastAPI, Flask, Django, React, Node.js, Streamlit, and Dash. Experience building Dockerized applications and Progressive Web Apps.
- Robotics & Robot Learning
Hands-on experience designing and building robotic systems and experimenting with imitation learning, Vision-Language-Action models, simulation, teleoperation, and learned robotic policies.
- Core Technologies
Python, PyTorch, PySpark, Pandas, Scikit-learn, SQL, Databricks, Airflow, Docker, GitLab CI/CD, AWS, Azure, Hugging Face, FastAPI, Django, React, Scala, and Java.
Erfahrung
Berufserfahrung0 - 5 Jahre
Freelancer seit2023
Sprachen
DeutschverhandlungssicherEnglischverhandlungssicherFranzösischverhandlungssicher
Projekthistorie
Developed statistical and machine learning models in Python to optimize Expedia pricing for hotels and flights, maximizing revenue and bookings. Applied constrained non-linear optimization, tree-based models (CatBoost, LightGBM), GLMs, and neural networks. Used Codex and Claude to interact with Databricks clusters and Trino SQL engines for large-scale analyses on datasets exceeding 1B rows. Designed and analyzed A/B tests and presented results to stakeholders. Worked within a team of 10 ML scientists across the full model lifecycle from R&D to production deployment on Airflow.
Research project and startup exploring state-of-the-art Vision-Language-Action policies for controlling robotic arms and autonomous mobile robots in warehouse-like environments. Developed and trained custom AI policies in PyTorch on self-collected datasets using cloud GPUs on RunPod, Hugging Face for model versioning, GitLab for code versioning, and Weights and Biases for experiment tracking. Deployed policies through a Django-React application with Docker and CI/CD pipelines. Showcased the project through a Django Progressive Web App, Blender animations, and custom 3D-printed robotic components.
Conducted applied research to evaluate the quality and business value of AI models. Developed and published Python packages exposing APIs for serving AI models. Conceptualized and presented IT infrastructure to support AI models in production for real-time model inference. Worked in an Agile team of 7 ML engineers using Jira and GitLab CI/CD pipelines. Maintained code quality through unit testing, code formatting, and Sphinx documentation.
Zertifikate
Azure Fundamentals (AZ-900)
Microsoft2023
Azure Data Scientist Associate (DP-100)
Microsoft2023
Azure AI Engineer Associate
Microsoft2023