Optuna Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who tune models, design search spaces, and run pruning workflows with Optuna, Hyperopt, and scikit-learn pipelines. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Optuna
Benjamin Matschke
Last position:
Founder, system architect, and main developer at Institute for Artificial Study (IAS)
- Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
- Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
- Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
- Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.
Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.
David Onaiyekan
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Amr Amer
Last position:
Machine Learning Engineer at German Research Center for Artificial Intelligence (DFKI)
- Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
- Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
- Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
- Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
- Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.
Dirk Markus M.
Last position:
Scientific Software Consulting Engineer
Technical audit for scientific software.
Ashkan Zadeh
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Mohammad Labeeb
Last position:
Research Intern - ML / ADAS at IAV GmbH
- Developed and optimized LSTM-RNN and Decoder Transformer models to predict vehicle trajectory during target loss events in Adaptive Cruise Control systems, achieving 20% improved predictive accuracy over baseline models.
- Engineered novel data preprocessing pipeline from real road campaign data, processing multi-sensor time series data, generating 300+ training snippets.
- Implemented Bayesian hyperparameter optimization and applied physical constraints to prevent model run-away behavior, resulting in 30% smoother acceleration profiles.
- Extended existing patented technology for AI-assisted ACC function improvements, building upon foundational work to enhance network performance.
- Tools: Python, TensorFlow, Keras, Optuna, CarMaker
Bhavani Savalam
Last position:
Data Partner - Computer Science - Digital Media at Telus Digital
- Innovative prompt engineer with expertise in generating and refining prompts specifically for computer science-related images.
- Proficient in developing responses that enhance machine learning models' understanding of visual data in the computer science domain.
Uddipan Basu Bir
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Ekaansh Khosla
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Discover over 15,000 top freelancers
Statistics of experts using Optuna
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
1.4 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
22%
Certifications per freelancer
1
Most common languages
German, English, Hindi
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Optuna
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What Optuna does
Optuna is a Python library for hyperparameter optimization. It helps teams search for better model settings, compare trials, and stop weak runs early. Companies use it for machine learning, forecasting, and other experiments where small tuning gains matter.
Common uses
- Tune tree-based models, neural networks, and classical ML pipelines
- Automate trial runs across different parameters and metrics
- Add pruning to save time on unpromising experiments
- Record study results for later review and repeatability
Ecosystem fit
Optuna works well with scikit-learn, PyTorch, XGBoost, LightGBM, and TensorFlow. Strong specialists know how to define good search spaces, connect custom objectives, and keep experiments reproducible. They also understand when Optuna is a better fit than Hyperopt or scikit-optimize.
When companies bring in freelancers
Teams usually need outside help when tuning becomes a bottleneck or model quality is inconsistent. That often happens during new ML product work, after a retrain, or when an internal team needs support on experiment design. In Germany, this is common in data-heavy teams that want focused remote help or short on-site sessions.
What strong specialists deliver
A good Optuna specialist does more than run trials. They shape the objective function, reduce wasted compute, and explain why one search strategy works better than another.
- Clean study setup and reproducible experiment flow
- Thoughtful parameter ranges and constraints
- Pruning, callbacks, and logging for better control
- Clear handover notes for internal teams
Signs you need this expertise
If model performance stalls, trial runs take too long, or parameter choices feel random, Optuna expertise helps. The right professional can turn ad hoc tuning into a structured workflow. That matters when you need better results without turning experimentation into a maintenance burden.
Frequently asked questions
Quick answers to the questions that come up most around Optuna.
Optuna is used to search for the best hyperparameters in machine learning and data science workflows. It fits tasks like model tuning, experiment comparison, and pruning weak trials early. Teams often use it when they need better results without manual guesswork.
Optuna is often chosen for its flexible study design, pruning support, and clean Python API. Hyperopt is also common for search, while scikit-optimize is often used in simpler scikit-learn-centric setups. The best choice depends on the model stack and how much control the team wants.
A strong Optuna specialist usually knows Python, experiment design, and at least one ML framework such as PyTorch, XGBoost, or LightGBM. They should also understand metrics, validation strategy, and reproducible runs. For larger setups, experience with distributed training or job orchestration helps.
A good Optuna freelancer needs access to the model, the objective, and the data split logic. Without that context, tuning can improve the wrong metric or overfit a validation set. The more complex the pipeline, the more important it is to review assumptions early.
Yes, Optuna work is often well suited to remote collaboration because most of it lives in code, notebooks, and experiment logs. That said, on-site sessions can help when teams need to align on modeling choices or review an existing pipeline. In Germany, many teams mix both depending on the project phase.
Good Optuna work produces clear search spaces, stable results across runs, and code that others can maintain. You should be able to see why certain parameters were chosen and how pruning or early stopping was applied. Quality also means the tuning process matches the real business goal, not just a single metric.
No, Optuna is useful well beyond deep learning. It also works well for gradient boosting, classical machine learning, and other parameter-heavy workflows. Many teams use it anywhere trial-and-error tuning is slowing them down.
Ask which framework they have tuned with Optuna, how they define the search space, and how they prevent overfitting on validation data. You should also ask how they handle pruning, logging, and reproducibility. Those answers reveal whether the specialist can improve the pipeline or only run experiments.
The average hourly rate of freelancers in Germany who have used Optuna in their recent projects is 62 €, which corresponds to a daily rate of about 495 € based on an 8-hour working day.
Of the freelancers in Germany who have used Optuna in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Germany who have used Optuna in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.4 years.
The most common languages among freelancers in Germany who have used Optuna in their recent projects are German (100%), English (100%), and Hindi (33%).
The most common industries among freelancers in Germany who have used Optuna in their recent projects are Information Technology (100%), Education (78%), and Manufacturing (56%).
The most common business areas among freelancers in Germany who have used Optuna in their recent projects are Information Technology (100%), Research and Development (100%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used Optuna
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
