Machine learning software engineer állás, munka

• You’re part of a Software Engineering Scrum team.
• You take the lead in our team for the further improvement of our ML models, as well as ensuring the models run reliably in production.

• Work with Product Managers and UX designers to specify new features and improve our products.
• Develop those new features with the end user in mind, and write unit tests and integration tests for them.
• Lead the definition of the architecture of our applications on AWS.

Design, develop, and deploy advanced ML models, with a focus on optimizing Large Language Models (LLMs) for diverse NLP and multimodal applications (text, image, structured data) and Visual Language Models (VLM)...

• Design and lead development of AI products for business-specific use cases.
• Mentor and guide the software engineering team, setting clear technical directions and providing architectural oversight.

• You will take technical responsibility for complex, cross-functional AI initiatives - from requirements analysis through to implementation.
• Furthermore, you will independently identify, evaluate, and manage AI-related projects.

• Defining the technical design of the system
• Refining and estimating Use Cases
• Implementation of domain and cross-cutting services in a team of up to 5 developers
• Close collaboration with Product Owners, Architects, and other Software Engineers

• You will take overall technical responsibility for complex, cross-functional AI initiatives - from requirements analysis through to implementation.
• You will independently identify, evaluate and manage AI-related projects.

• You create advanced AI approaches for general-purpose robotics, with a focus on robotic foundation models.
• Furthermore, you train, finetune, evaluate, and deploy models such as vision-language-action architectures.

Build high-performing, AI-enabled teams that deliver real value fast—especially within our Budapest Global Center for Product Innovation Lead engineering for one or more of our strategic products, driving design, delivery, and quality across multiple agile teams Define technical strategy, system...

• Design, configure, and optimize interactions with LLMs through retrieval-augmented generation (RAG), and orchestration.
• Implement tool-calling patterns to integrate LLMs with SaaS tools and APIs.
• Leverage fine-tuning or parameter-efficient techniques for performance enhancement.

• Partner with stakeholders to identify, design and evaluate AI solutions by translating complex business challenges into practical ML and GenAI use cases
• Lead data-driven projects, from requirements analysis to implementation, with a strong focus on business impact