WORK EXPERIENCE

LEADIQ - Machine Learning Engineer (January 2022 – Present; San Francisco, CA)

Tech Stack: PyTorch, GPT, OpenAI API, Strawberry, FastAPI, Flask, Streamlit, Transformers, HuggingFace, Langchain, Gitlab, Linux, AWS, MongoDB, Docker, Kubernetes, Datadog, Redis, Redshift, Databricks, PySpark

7.2022 - current: Used LLMs to generate personalized prospecting emails that helped SDRs to reach out to 10x more prospects with competitive open & reply rates. This is a product level project and includes data collection, LLM fine tuning, prompt engineering, tracking metrics, creating performance & usage dashboards and alerts
- Regarding model training; utilized with many different fine-tuning techniques to increase performance including upside-down RL / decision transformer style, weighting of user feedback data, various sampling strategies to reduce entropy collapse and improve variety
- Improved the quality of emails generated by utilizing automated user feedback to either fine-tune or improve prompting of our LLMs
4.2025 - current: Training an ML model & pipeline to score Accounts for RevOps & AEs & SDRs. Also working on using LLMs to create account stories for SDRs, so they can take automated actions.
9.2024 - 3.2025: Trained ML model to predict revenues of private companies, using historical data. Error metric is computed in percentage mean absolute error and also percentiles of it. It’s deployed as a Databricks Unity Catalog Model.
1.2024 - 8.2024: Created Databricks pipeline that computes daily open & reply rate statistics of generated emails using PySpark. Pipeline includes matching Salesforce data to local data, API integration, queries to various ML models and processes thousands of emails daily.
4.2022 - 6.2022: Trained ML Model (LSTM) to classify job titles into seniority & function buckets. Achieved 90% classification accuracy among 6 seniority classes and 70% accuracy among 50 job functions. It’s deployed on both Kubernetes (for single inference) and Databricks (for large batch inference) and widely used in production at a high scale.
1.2022 - 3.2022: Trained ML model (Siamese Network) to learn similarities of job titles. Achieved 97% classification accuracy
Functioning Backend Engineer: Aside from ML work above; designed and developed GraphQL APIs, wrote typesafe code with pydantic/mypy, wrote integration tests and unit tests, reviewed merge requests, inspected and resolved errors from datadog logs and customer complaints, collaborated with project manager and other team members, wrote documents & reports etc.

SESTEK - Senior AI Research Engineer (March 2020 – December 2021; Istanbul, Turkey)

Tech Stack: PyTorch, BERT, LSTM, FastAPI, Transformers, HuggingFace, Linux, Docker, Azure DevOps, Azure Cloud

Trained various types of model architectures (CNNs, RNNs etc.) for speech classification tasks (age & gender & emotion detection, spoofing detection for voice biometrics etc.)
Fine-tuned kaldi library for task specific speech recognition models.
Conducted comprehensive literature reviews and wrote reports to ensure products stayed up-to-date
Organized regular meetings with auditors for R&D projects to ensure the continuation of the investments

SESTEK - Research and Development Engineer (March 2017 – March 2020; Istanbul, Turkey)

Trained chatbot intent detection text classifier model using PyTorch, Hugging Face libraries, LSTM, and Google BERT architectures, delivering 85% intent detection accuracy to the chatbot application of one of Turkey’s largest banks
Developed a sequence-to-sequence LSTM model that efficiently transformed textual representations of numeric values such as addresses, dates, and times into formatted forms, leading to a publication on this subject
Actively involved in every stage of the ML Pipeline: data cleansing, training, evaluation, testing on real customer data, and deploying trained models as REST services using Python

EDUCATION

MSc - BOGAZICI UNIVERSITY - Electrical and Electronics Engineering (GPA: 3.69/4.00)

Research Focus: Automated Response Generation for Corporate Chatbot Systems
Specialized Courses: Pattern Recognition, Speech Processing, ML, Statistical Signal Analysis, Social Semantic Web

BSc - BOGAZICI UNIVERSITY - Electrical and Electronics Engineering (GPA: 3.28/4.00)

Core Courses: Probability, Matrix Theory, Signal Processing, Introduction to Image Processing

TECHNOLOGIES

Python : PyTorch, Langchain, OpenAI API, Strawberry, FastAPI, Flask, Streamlit, Chainlit, Numpy, Pandas, Pydantic etc.
AI/ML : GPT , Transformers, LSTM, BERT, Hugging Face, Llama, LlamaIndex, Mlflow
Data Analytics : Databricks, PySpark, Redshift
CI/CD : Gitlab, Github, Docker, Kubernetes, Datadog, Insomnia, Redis, Slack, Jira, AWS, MongoDB, Azure DevOps

PUBLICATIONS

A. S. Güser, M. Erden and M. L. Arslan, “Semi-Automatic Formatting of Spelled Out Numbers” 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, 2019

CERTIFICATES

ADDITIONAL

Achievements : Ranked 6th among 1.7m students in the National University Entrance Exam (2012)
Activities : Walking, Literature, Traveling

Abdullah Şamil Güser