LLM Researcher
At a glance
Mid-level AI Research role at Oddin. Remote · contract.
Pay not stated AI Research salary
remoteroles summary, based on the employer's posting.
What you'll do
- Adapt existing language models or train new ones for customer-facing systems and the listed use cases
- Turn promising prototypes into dependable services that can be maintained and observed in production
- Build retrieval-based systems using vector or graph databases, then connect them to the wider solution
- Deploy and monitor models that support live game commentary or interactive avatars
What you bring
- You have solid hands-on experience with language models already running in production
- You have built retrieval-augmented systems and used vector or graph databases
- You can deploy, monitor and integrate these systems, with a strong grasp of function calling and structured outputs
- You are proficient in Python and understand agent workflows involving reasoning and tool use
Who this fits
This remote contractor role suits someone who has taken language-model systems from development into production. You can also work from the Prague office.
From the employer
About the Role
We are looking for an experienced LLM Researcher to help us build intelligent, automated systems that enhance customer-facing applications. In this role, you are expected to utilize, optimize and finetune existing language models or train new ones, more adapted to our use cases - and turn successful prototypes into reliable, maintainable, and observable production services. Your work will directly contribute to the development of dynamic, real-time commentary content for games such as FIFA, NBA, or interactive avatars.
This is a remote role with an option to join our Prague office, offering the opportunity to work with cutting-edge LLM frameworks, retrieval-augmented generation (RAG), and multimodal models that process both language and vision. You will collaborate with a small, fast-moving team of ML engineers, product developers, and domain experts to design systems that combine reasoning, tool use, and creative content generation.
Responsibilities
- Solid experience with LLMs in production environments, including training, fine-tuning, inference, and tool integration.
- Experience building RAG-based systems and working with vector or graph databases.
- Experience deploying, monitoring and integrating these systems into the overall solution.
- Strong understanding of function calling, structured output generation, and agentic reasoning workflows.
- Proficiency in Python and key libraries (e.g., PyTorch, Hugging Face Transformers, FastAPI).
- Experience with sports broadcasting or data-driven content generation (e.g., live commentary, analytics).
- Familiarity with multimodal LLMs and integrating textual and visual inputs.
- Familiarity with tools like vLLM, Triton, or DeepSpeed for efficient model serving.
- Contributions to open-source AI tools or research publications in NLP, multimodal AI, or agent systems.
Must-have:
Nice-to-have:
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