Sahika Genc
Principal Applied Scientist at Amazon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSahika Genc is a Principal Applied Scientist based in Seattle with over 6 years at Amazon and AWS driving foundational AI across advertising, search, and robotics. She leads mid- and post-training of large models for agentic reasoning, analytics, and coding for Amazon’s advertiser business, and helped build Amazon’s multilingual instruction-tuning and preference-optimization pipelines. Sahika architected whole-page optimization using multi-agent RL and bandits for Amazon Search and spearheaded the international rollout of Rufus, Amazon’s AI shopping assistant. Earlier she co-created AWS DeepRacer and advanced multi-agent coordination and sim-to-real generalization, publishing at ICRA and NeurIPS and contributing hands-on DeepRacer analysis tooling on GitHub. Her background blends a PhD in Systems from the University of Michigan with decades of cross-domain ML and control experience, enabling both research publications and production-grade AI systems.
6 years of coding experience
19 years of employment as a software developer
PhD, Electrical Engineering: Systems, PhD, Electrical Engineering: Systems at University of MichiganB.S., Electrical Engineering, B.S., Electrical Engineering at Orta Doğu Teknik Üniversitesi / Middle East Technical University
Github Skills (7)
amazon-sagemaker10
jupyter-notebook10
python10
reinforcement-learning10
log-analysis9
deep-learning8
amazon-cloudwatch8
Programming languages (2)
Github contributions (5)
aws-solutions-library-samples/guidance-for-training-an-aws-deepracer-model-using-amazon-sagemaker
Nov 2019 - Oct 2020
DeepRacer workshop content. This Guidance demonstrates how software developers can use an Amazon SageMaker Notebook instance to directly train and evaluate AWS DeepRacer models with full control
Role in this project:
ML Engineer
ML EngineerContributions:35 commits, 2 PRs, 35 pushes in 11 months
Contributions summary:Sahika's commits primarily focus on the analysis and evaluation of AWS DeepRacer models trained using Amazon SageMaker. They developed and utilized Python notebooks to analyze log files, parse checkpoint data, and assess the performance of different training runs. Their work involved downloading and processing simulation logs, and extracting key metrics such as laps completed.
amazon-sagemakeraws
aws-samples/sagemaker-rl-procgen-ray
Aug 2020 - Sep 2020
Contributions:39 commits, 5 pushes in 23 days
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