Daniel is available for hire
Hire DanielDaniel Nouri
Verified Expert in Engineering
Machine Learning Developer
Location
Malmö, Sweden
Toptal Member Since
January 8, 2019
Daniel is a machine learning specialist focusing on deep learning, a software engineer with over 18 years of experience building reliable, high-performing systems, and the owner of Natural Vision UG, based in Berlin, Germany. An exceptional communicator and self-starter who's contributed to many projects over the years (including scikit-learn), Daniel joined Toptal to find work that piques his interest.
Portfolio
Securitas
Azure, Databricks, OpenAI GPT-3 API, OpenAI GPT-4 API, Machine Learning...
AT&T
WebRTC, Docker, Azure, Machine Learning, Artificial Intelligence (AI)...
SmartThings
C++, Python, Machine Learning, Artificial Intelligence (AI)
Experience
Availability
Part-time
Preferred Environment
Emacs, Git, Linux
The most amazing...
...thing I've done was leading a team of researchers/engineers to improve a state-of-the-art app with real-time medical image analysis.
Work Experience
Technical Team Lead (Consultant)
2022 - PRESENT
Securitas
- Organized and conducted workshops on utilizing large language models with retrieval techniques, empowering participants with the knowledge and skills to leverage these powerful tools effectively.
- Spearheaded the use of generative pre-trained transformer (GPT) and related technologies within the organization, driving innovation and implementing novel use cases across various domains.
- Worked with cross-functional teams to ensure seamless integration of machine learning models into existing systems.
- Mentored and guided team members, fostering professional growth and promoting a collaborative and innovative work environment.
- Spearheaded the implementation of rapid prototyping methodologies, enabling the team to quickly iterate and validate ideas, resulting in faster development cycles and enhanced product quality.
Technologies: Azure, Databricks, OpenAI GPT-3 API, OpenAI GPT-4 API, Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), Data Science, Generative Pre-trained Transformers (GPT), GPT
Lead Principal, Systems Engineering
2022 - 2022
AT&T
- Implemented a web component that attaches to WebRTC video chats and forward audio streams to emotion and speech recognition systems.
- Added emotion recognition based on audio (not text) in a new call center product, including research and implementation of an ML algorithm and cloud architecture design.
- Evaluated and integrated speech recognition systems for new call center products.
- Established best practices for developing machine learning systems, including evaluation, software testing, and continuous integration.
Technologies: WebRTC, Docker, Azure, Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), Data Science
Machine Learning Consultant
2021 - 2022
SmartThings
- Built a custom deep learning model for efficient motion detection on edge based on H264 motion vectors.
- Created a core framework for delivering ML models on edge in C++, supporting high efficiency, plug and play, model chaining, and multi-platforms.
- Implemented interactive tooling to improve data quality.
- Worked closely with the data science team, including project leadership and mentoring.
Technologies: C++, Python, Machine Learning, Artificial Intelligence (AI)
Machine Learning Lead | Software Engineer | Trainer
2014 - 2021
Natural Vision UG
- Developed and deployed a predictive analytics system for parcel delivery for a Fortune Global 500 company.
- Developed a predictive analytics system for sales forecasts which outperforms human analysts in terms of speed and accuracy for a Fortune Global 2,000 company.
- Developed a deep-learning-based bioacoustics recognition system for detecting and classifying marine mammals in collaboration with Oregon State University.
- Helped Jetpac, a company that built city guides using AI, implement a deep-learning-based computer vision system that finds objects inside millions of Instagram photos. The extracted info was then used to automatically categorize.
- Helped build up an in-house corporate data science team at a Fortune Global 500, teaching software engineering best practices and guiding a research-oriented team toward developing robust, maintainable, and production-ready software.
Technologies: Scikit-learn, Python, Deep Learning, Docker, Deployment, CI/CD Pipelines, Data Science, Computer Vision, Image Recognition, Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP)
Principal Software Engineer
2020 - 2020
Cloud Governance Startup
- Worked on building an open-source project to enable testing of cloud policy configuration rules written in Terraform and CloudFormation before deployment.
- Worked on several bugfixes and improvements on the company's open-source software.
- Developed onboarding documentation for a rapidly growing team.
Technologies: Python, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP), Terraform, AWS CloudFormation
Lecturer
2014 - 2018
Data Science Retreat
- Regularly gave courses around Python, Scientific Python, machine learning, and deep learning.
- Developed course material and hands-on tutorials.
- Advised students on portfolio projects and career choices.
Technologies: Machine Learning, Python, Data Science
Chief Scientist
2016 - 2017
Samsung Research America (Samsung NEXT)
- Researched and developed algorithms for the estimation of liquid flow inside of water pipes, using an IoT sensor that is attached to the outside of the pipe.
- Implemented signal processing and machine learning algorithms for lower-power, embedded devices.
- Built a rig for the semi-automatic acquisition of labeled training data for water flow detection; using Raspberry Pi, an inline flow meter for ground truth, and several sensors.
- Developed low-power, digital wireless communication protocols. Built a system for remote maintenance of sensors and communication hubs.
Technologies: Internet of Things (IoT), C, C++, Machine Learning, Microcontrollers, Python, Data Science
Head of Machine Learning
2015 - 2016
Butterfly Network
- Led a data science and engineering team in building medical imaging applications for ultrasound.
- Acted as team lead and reported directly to the company president.
- Grew our team successfully from three to six people.
- Worked with doctors and the product team to refine products and features.
- Built several deep learning models and built systems for data acquisition and annotation.
Technologies: Medical Imaging, Python, Convolutional Neural Networks (CNN), Amazon Web Services (AWS), NumPy
Experience
Skorch
https://github.com/dnouri/skorchA Scikit-learn compatible neural network library that wraps PyTorch.
Nolearn
https://github.com/dnouri/nolearnNolearn combines the ease of use of Scikit-learn with the power of Theano/Lasagne. It was the first deep learning framework with an emphasis on user friendliness and was used to win several international competitions. 1000 stars and 16 contributors.
Using Convolutional Neural Nets to Detect Facial Key Points Tutorial
http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/This is a hands-on tutorial on deep learning. Step by step, we'll go about building a solution for the facial key point detection Kaggle challenge.
The tutorial introduces Lasagne, a new library for building neural networks with Python and Theano. We'll use Lasagne to implement a couple of network architectures, talk about data augmentation, dropout, the importance of momentum, and pre-training. Some of these methods will help us improve our results quite a bit.
The tutorial introduces Lasagne, a new library for building neural networks with Python and Theano. We'll use Lasagne to implement a couple of network architectures, talk about data augmentation, dropout, the importance of momentum, and pre-training. Some of these methods will help us improve our results quite a bit.
Using Deep Learning to Listen for Whales
http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/Here I worked with Oregon State University on detecting whale calls in underwater audio recordings.
Palladium
https://github.com/ottogroup/palladiumA framework for setting up predictive analytics services.
Skills
Languages
Python, C, C++, SQL, JavaScript
Libraries/APIs
PyTorch, Scikit-learn, TensorFlow, Pandas, NumPy, OpenCV, WebRTC
Tools
Git, Emacs, Pytest, Terraform, AWS CloudFormation
Paradigms
Automated Testing, Pair Programming, Agile, Data Science
Platforms
Docker, Linux, Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), Databricks
Other
Artificial Intelligence (AI), Deep Learning, Image Recognition, Predictive Analytics, Convolutional Neural Networks (CNN), Medical Imaging, CTO, Debugging, Machine Learning, Computer Vision, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Computer Vision Algorithms, Time Series Analysis, Sensor Data, Hardware, Signal Processing, Full-stack, Microcontrollers, Internet of Things (IoT), Deployment, CI/CD Pipelines, OpenAI GPT-3 API, OpenAI GPT-4 API
Storage
PostgreSQL
Frameworks
Spark
Certifications
JANUARY 2012 - PRESENT
Neural Networks for Machine Learning
University of Toronto via Coursera
JANUARY 2012 - PRESENT
Machine Learning
Stanford University via Coursera
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