Oddin

B2B esports betting odds provider

LLM Researcher

Natural Language Processing & LLMsUpdated on Nov 10

Full-timeNo salary listed

About the Job

Requirements

Solid experience with large language models in production environments, including training, fine-tuning, inference, and tool integration.
Experience building retrieval-augmented generation 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, PyTorch, Hugging Face Transformers, and FastAPI.

Responsibilities

Utilize, optimize, and fine-tune existing language models or train new models adapted to the use cases.
Turn successful prototypes into reliable, maintainable, and observable production services.
Build systems that generate dynamic, real-time commentary content for games and interactive avatars.
Collaborate with machine learning engineers, product developers, and domain experts to design systems combining reasoning, tool use, and creative content generation.

Desired Qualifications

Experience with sports broadcasting or data-driven content generation, such as live commentary or analytics.
Familiarity with multimodal large language models and integration of textual and visual inputs.
Familiarity with vLLM, Triton, or DeepSpeed for efficient model serving.
Contributions to open-source artificial intelligence tools or research publications in natural language processing, multimodal artificial intelligence, or agent systems.

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

Must-have:

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).

Nice-to-have:

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.

By applying to this position, you consent to Valka.AI s.r.o., ID No.: 231 32 221, Pod dráhou 1637/6, Holešovice, 170 00 Praha 7, Czech Republic together with it's subsidiaries processing your personal data under Regulation (EU) 2016/679.

Your data will be processed for recruitment and may be retained for up to 3 years for similar job opportunities.
Rights: Access, correct, delete, restrict, or object to processing; withdraw consent via [email protected]; or complain to www.uoou.cz.

About the Company

Oddin

B2B esports betting odds provider

Oddin.gg provides B2B esports betting solutions, including esports odds feeds, risk management, and iFrame integrations for betting operators. Its product works by delivering real-time odds feeds, automated risk controls, and embeddable betting interfaces powered by machine learning and data science to optimize partner profitability and user engagement. The company differentiates itself from competitors through a partner-focused model that emphasizes ML-driven odds and risk management, reliable uptime, and a wide range of live markets delivered via easy-to-integrate interfaces. Its goal is to help betting operators grow profitability and deliver a compelling esports betting experience.

Company size: 51-200
Company stage: Series A
Total funding: $8.2M
Headquarters: Prague, Czechia
Founded: 2018

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