Top Machine Learning Jobs in Turkey

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Build and own production ML and LLM systems for onboarding, risk, compliance, and operations. Design models, engineer features from messy data, balance performance and interpretability, integrate with backend training/inference pipelines, write tested code, and partner with product and operations to drive scalable, reliable automation.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Develop and evaluate generative and predictive deep learning models for protein and nucleic acid design; curate large biomolecular datasets; collaborate with computational and experimental teams to validate models and shape company ML strategy.

The intern will develop AI models, enhance neural mechanism understanding, and collaborate on generative modeling while maintaining code quality.

Design and maintain scalable AI systems using LLMs, build applications, collaborate with teams, and improve agent quality and performance.

The Machine Learning Engineer will streamline ML models to production, maintain, monitor models, and design/build models while collaborating with data scientists.

The ML Platform/MLOps Engineer will build and maintain infrastructure for large-scale ML systems, ensuring efficiency, reliability, and security while supporting the research team from experiments to production models.

As a Robotics Machine Learning Engineer, you will build data pipelines, run demos, and ensure customer satisfaction while evolving product features based on client needs.

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

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.

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.

The Senior ML Engineer will design, build, and optimize ML models, focusing on recommender systems and MLOps, primarily using AWS.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

As a Machine Learning Engineer at Nucs AI, you'll develop and deploy ML models for medical imaging, ensuring regulatory compliance and collaborating closely with engineering teams.

Design and build simulation environments for robotics research, implement RL algorithms, and evaluate policies for stability and transfer potential. Collaborate with teams and document results.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.