ML / AI Engineer İstanbul, Türkiye

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(Uppdaterat 2021-12-27)

ML / AI Engineer

İstanbul, Türkiye

Modersmål English

  • + 7 years of experience in Computer Vision
  • + 7 years of experience in Python
  • + 7 years of experience in Machine Learning
Kontrakt

Kompetenser (16)

SQL

Machine Learning

Big Data

Huggingface

Data Analysis

NEURAL NETWORKS

TENSORFLOW

Pandas/ScikitLearn

SENSORS

XgBoost/LightGBM

GCP

Pytorch

Artificial Intelligence

AWS

COMPUTER VISION

STATISTICS

Professionell bakgrund

Machine Learning Engineer
Turing.com

2021-01 - Pågående

(San Francisco - Remote) • Turing.com is a company with 60+ M$ funding that focuses on matching remote developers with US based companies for full-time job opportunities. Worked on feature engineering such as resume parsing and extracting signal from developer profiles for predicting accurate probability values for individual developers to get matched and start working with a remote company. Owned ranking related features, where each company were recommended specific developers for their requirements. Developed and optimized Elasticsearch ranking function to directly increase company KPIs.
Artificial Intelligence Engineer
SenpAI.GG

2020-08 - 2021-02

Big data analysis of League of Legends (LoL) matches, where feature engineering with in-game statistics and match outcome is performed. Using statistical approach individual players game performance is evaluated on the SenpAI.GG application for e-sports online coaching purposes. Deployed predictive model for the entire LoL 2020 Worlds Championship were predictions for each game in the tournament had an accuracy of %71.4. Developed computer vision features for Valorant game that works on the client side. Also trained deep learning models that analyses LoL match replays for detecting key events, which were extracted as clips and served to the users for visual e-sports guidance.
Artificial Intelligence Engineer
Omnisight

2019-08 - 2020-07

Deployment of deep neural networks to be used in computer vision tasks. DNNs consists of human detection, face detection, gender/age classification and pose estimation models. Ad-hoc optimization is performed in order to run models on the edge in NVIDIA Jetson products with higher throughput, like TensorRT conversion and model complexity reduction. Multiple-object human tracking was also deployed and end statistics was produced and reported to be provided to the customers.
Machine Learning Engineer
Science Wave Capital

2018-09 - 2019-03

Model building that is used in predicting stock specific future returns/5-day alpha values for a market neutral portfolio, mainly adopted ensemble of boosting algorithms (LightGBM/XGBoost) and neural-networks. Big data consisting of 20 years of daily stock specific information listed in European stock exchanges were used. Feature Engineering tasks relating to extracting/testing new signals due to the changing market regimes and ensuring model quality in high volatility periods.
Machine Learning Engineer
Infotech

2016-11 - 2018-09

Infotech • Project specific work, such as processing information from sensors (Lidar / Stereo Camera) and Fusion for TEMSA MD9 electriCITY Autonomous bus was followed. The main focus was on computer vision tasks such as object detection and multiple object tracking, stereo disparity map extraction. Machine learning models were deployed for TEYDEB projects: Indoor localization using smart phones and observed RSSI values, Driver risk classification using telematics - collection of accelerometer and GNSS data obtained from individual drivers- data collected in Turkey. @@Kaggle Competitions Global Wheat Detection | 4th out of 2245 Santander Customer Transaction Prediction | Top% 1.5 out of 8800

Akademisk bakgrund

M.Sc. in Computer Science & Engineering
Galatasaray University

2015-01 - 2017-01

B.S. in Computer Science
Sabanci University

2009-01 - 2014-01

High School Diploma
Izmir American College

2005-01 - 2009-01

Kontakta konsult

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