Mikayel Samvelyan, Machine Learning Developer in London, United Kingdom
Mikayel Samvelyan

Machine Learning Developer in London, United Kingdom

Member since May 1, 2018
With a master's degree from the University of Oxford, Mikayel is a machine learning expert and data scientist specializing in natural language processing, computer vision, and reinforcement learning. He has substantial experience in research and development of deep learning solutions and has worked at such companies as Reddit, Mentor, and USC. Mikayel has co-authored several scientific publications at leading machine learning conferences.
Mikayel is now available for hire




London, United Kingdom



Preferred Environment

PyCharm, Vi

The most amazing...

...project I've worked on is developing a new AI algorithm that masters the game of StarCraft II.


  • Research Engineer (Computer Vision)

    2020 - 2020
    USC Information Sciences Institute (via Toptal)
    • Built a scalable pipeline for a video prediction models that runs parallel on multiple remote GPU instances.
    • Performed a pose estimation and projected 3D objects to their corresponding 2D projections in the videos.
    • Developed a motion detection software using Python and OpenCV.
    • Constructed a head pose estimation library using OpenCV and Dlib.
    • Developed a real-time object detection library based on YOLO.
    • The work has been presented to DARPA during the on-site visit to USC ISI.
    Technologies: OpenCV, Python
  • Machine Learning Expert

    2019 - 2019
    Reddit (via Toptal)
    • Developed deep-learning solutions for large-scale natural language processing tasks using PyTorch.
    • Fine-tuned pre-trained NLP models, such as BERT, XLNet, and RoBERTa.
    • Conducted topic modeling experiments using methods such as LDA, NMF, and so on.
    • Built a modular pipeline for large-scale natural language classification tasks.
    • Analyzed large-scale datasets using BigQuery.
    Technologies: Amazon SageMaker, Custom BERT, PyTorch, Python
  • Data Scientist

    2018 - 2019
    Highlander Technology, Inc. (via Toptal)
    • Developed and optimized deep learning solutions for large scale natural language processing tasks using PyTorch.
    • Implemented noise and novelty detection on a large corpus of data.
    • Performed rigorous data mining for raw and noisy data.
    • Created an API endpoint to use the model for multiclass classification.
    • Built a machine learning pipeline for large-scale multiclass classification.
    Technologies: Amazon SageMaker, PyTorch, Python
  • Machine Learning Consultant

    2018 - 2018
    Mission Ready Marketing, LLC (via Toptal)
    • Designed a pipeline for machine learning methods to be used in a recommendation engine.
    • Examined the methods for building a recommendation system with unsupervised learning techniques for settings without historical data.
    • Analyzed item-based, user-based, matrix factorization-based, and hybrid recommender systems.
    • Created a full technical approach documentation for connecting the learning component of the project with the database and back-end.
    Technologies: Python
  • Machine-learning Researcher

    2018 - 2018
    • Searched and identified objects on satellite, drone, and ground-based images.
    • Designed and implemented a deep reinforcement learning algorithm for large-scale fleet management.
    • Created a simulator for a ride-hailing service using Python.
    • Built a machine learning framework for predicting a user's intentions.
    Technologies: Google Vision API, Sacred, PyTorch, Python
  • Research and Development Engineer

    2014 - 2016
    Mentor Graphics
    • Conducted research about algorithms for logic optimization, partitioning, placement, and routing.
    • Developed a unified environment for design capturing, simulation setup, verification, and analysis for a custom integrated circuit design platform.
    • Integrated the environment with third-party IDE software.
    • Created various kinds of reusable compiler-compilers using Lex and Yacc.
    Technologies: Lex, Yacc, C++
  • Software Engineer

    2012 - 2013
    Instigate Robotics
    • Built firmware and software for robotic applications.
    • Developed 3D printing technologies.
    • Created an application's graphical user interface (GUI) with C++ and Qt.
    • Designed and implemented various embedded applications on STM32 and Arduino MCUs.
    • Built an educational development environment for robotics.
    Technologies: Qt, Smalltalk, C++, C
  • Software Engineering Intern

    2012 - 2012
    Instigate Design
    • Developed a hard/software independent environment for parallel computing on multiprocessor computers.
    • Supported the development of a compiler's front end.
    Technologies: Clang, C++


  • QMIX: Monotonic Value Function Factorization for Deep Multi-agent Reinforcement Learning

    QMIX is a state-of-the-art value-based algorithm for collaborative deep multi-agent reinforcement learning for the settings of centralized training with decentralized execution.
    QMIX employs a neural network that estimates joint action-values as a complex nonlinear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximization of the joint action-value in off-policy learning, and guarantees consistency between the centralized and decentralized policies.

    QMIX was presented at ICML 2018, one of the leading conferences for machine learning research.

  • PyMARL: Python Multi-agent Reinforcement Learning

    PyMARL is a research platform for deep multi-agent reinforcement learning which allows out-of-the-box experimentation and development.

    Written in PyTorch, PyMARL features implementations of several state-of-the-art methods such as QMIX, COMA, and independent Q-Learning.

    In collaboration with the Berkeley AI Research lab, some of the above algorithms have also been successfully ported to the scalable RLlib framework.

  • SMAC: The StarCraft Multi-agent Challenge

    The StarCraft Multi-agent Challenge (SMAC) is a benchmark for cooperative multi-agent reinforcement learning (MARL) that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.

    Paper: https://arxiv.org/abs/1902.04043
    Blogpost: http://whirl.cs.ox.ac.uk/blog/smac/
    Blogpost by NVIDIA: https://nvda.ws/2I88F1I

    SMAC was presented at AAMAS 2019, the largest and most influential conference in the area of agents and multiagent systems.

  • ADAM Visual Perception

    Our learning process depends on having a fairly rich (though developmentally plausible) input representation. This repository explores how two aspects of visual perception which are vital for early language learning can be captured by algorithms, namely motion cause detection and gaze object detection.


  • Languages

    C++, C#, Python, Java, Lua, SQL, R, C, Objective-C, Smalltalk, Assembler x86, Prolog, Lisp, JavaScript, Octave
  • Frameworks

    Flask, .NET, Qt, Spark
  • Libraries/APIs

    SciPy, NumPy, Pandas, PyTorch, TensorFlow, Scikit-learn, Keras, OpenCV, Spark ML, PySpark, Google Vision API, Google APIs
  • Tools

    Jupyter, LaTeX, Makefile, Vim Text Editor, Subversion (SVN), Git, PyCharm, CVS, MATLAB, Tmux, Tableau, Sacred, BigQuery, Amazon SageMaker, AutoML
  • Platforms

    Unix, Docker, MacOS, Linux, Amazon EC2, Amazon Web Services (AWS)
  • Other

    System Programming, Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), Custom BERT, Statistics, Classification, Predictive Modeling, Machine Learning, Mathematics, Data Visualization, Robotics, Computer Vision, Recommendation Systems, Big Data, Image Processing, Physics, Vi, Yacc, Lex, Clang, Time Series Analysis
  • Paradigms

    Requirements Analysis, Design Patterns, Data Science
  • Storage

    MongoDB, PostgreSQL, Redshift


  • Master of Science Degree in Computer Science
    2016 - 2017
    University of Oxford - Oxford, UK
  • Master of Science Degree in Computer Science and Applied Mathematics
    2014 - 2016
    Yerevan State University - Yerevan, Armenia
  • Bachelor of Science Degree in Computer Science and Applied Mathematics
    2010 - 2014
    Yerevan State University - Yerevan, Armenia
  • Undergraduate Exchange in Computer Science
    2013 - 2013
    Delta State University - Cleveland, MS, USA


  • CS190.1x: Scalable Machine Learning
    The University of California, Berkeley via edX
  • Algorithms: Design and Analysis, Part 2
    MAY 2015 - PRESENT
    Stanford University via Coursera
  • Algorithms: Design and Analysis, Part 1
    APRIL 2015 - PRESENT
    Stanford University via Coursera

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