Top Machine Learning Jobs in Turkey

The Account Executive will drive new business by selling SaaS solutions to Managed Service Providers in the Nordics, managing the full sales cycle, and achieving revenue targets.

Lead the LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

The Associate will perform research, build analytical models, learn consulting basics, and take part in a defined training program.

The Senior IS Auditor will lead compliance programs, execute audits in IT, and enhance security governance while advising teams on secure practices.

As a Customer Success Manager, you will oversee Turkish accounts, ensuring value realization from digital twin technology, managing relationships, and identifying upselling opportunities.

Develop AI products, manage data pipelines, write production-ready code, monitor system performance, mentor engineers, and foster collaboration across teams.

Design and implement AI products/features, manage data pipelines, write production-ready code, and ensure system performance. Requires problem-solving and knowledge of AI/ML concepts.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Design and develop innovative deep learning methods for biomolecular design, collaborating with biology teams, prototyping new algorithms, and presenting results.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

Lead the design and development of Machine Learning solutions, manage project timelines, collaborate with stakeholders, mentor team members, and engage in research and development.

The ML Developer will build statistical models, analyze data, implement algorithms, and collaborate with teams to enhance clinical trial processes using advanced analytics.

The Machine Learning Intern will work on applied computer vision pipelines, experiment with ML models, write well-structured code, and participate in design sessions.

The Senior Machine Learning Engineer will implement and enhance ML solutions, working closely with various business departments to deliver scalable applications and improve MLOps practices.

Build scalable backend and data services for ML feature storage, model training, and real-time inference. Design APIs, ensure data quality, monitor performance, implement security, and collaborate with data scientists and product managers.

The Tech Lead will architect and implement AI systems, oversee model integration and ensure compliance with security standards while collaborating with teams on scalable solutions.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As an AI/ML Specialist Solutions Architect, you will design scalable AI solutions, manage AI deployments, and collaborate with clients to enhance customer satisfaction.

Build, productionize, maintain, and monitor machine learning models and pipelines. Collaborate with data scientists on model design, data preprocessing, performance tuning, deployment, and observability. Support MLOps practices and ensure software engineering standards are followed.

The Machine Learning Engineer will design, develop, and implement machine learning algorithms, collaborate with teams, and ensure efficient model deployment and monitoring.