LLM Data Strategy Expert; Annealing​/SFT

About Bitdeer

Bitdeer is a world-leading technology company for Bitcoin mining and AI cloud.

Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers. Apart from designing industry-leading ASIC chips and manufacturing mining rigs, the Group handles complex processes involved in computing across the value chain. This includes equipment procurement, transport logistics, datacenter design and construction, equipment management, and network and facility operations. Bitdeer also offers advanced cloud capabilities to customers with a high demand for artificial intelligence.

Headquartered in Singapore, Bitdeer operates globally with a diversified 3 GW energy portfolio, and deploys Bitcoin mining and HPC datacenters in the United States, Bhutan, Norway, Canada, Malaysia, and Ethiopia.

Bitdeer AI Lab is a frontier AI lab under Bitdeer, a global‑leading computing power solutions provider. Guided by long‑termism, we are committed to exploring the frontiers of artificial intelligence with the ambition, courage, and determination to build technologies that can truly change the world.

We believe that transformative breakthroughs in AI require both long‑horizon thinking and relentless execution. Our mission is twofold: first, to effectively transform energy into intelligence; second, to push the limits of intelligence by rethinking AI systems and architectures that can learn more efficiently, reason more deeply, and scale more effectively.

Our vision is to create intelligence that learns more like humans do: efficiently, adaptively, and recursively, turning finite parameters and finite compute into unbounded potential. We pursue this work with a deep sense of purpose, believing that the most meaningful advances in AI will not only push the frontier of research, but also reshape the future of the world.

Our lab is equipped with thousands of cutting‑edge GPUs dedicated to AI research, and we are committed to continuously investing in and expanding our computational infrastructure to support world‑class research and engineering in artificial intelligence.

We are looking for exceptional talent to join us, helping build the data foundation for frontier AI models. This role is centered on building the data foundation for LLMs. You will own the end‑to‑end pre‑training and post‑training data pipeline, including data sourcing (open‑source datasets and, where needed, web‑scale crawling), large‑scale cleaning and quality filtering (deduplication, formatting, sampling, and quality classification), data mixture design to find the optimal recipe across domains and stages, data validation through small‑scale proxy runs and downstream evaluation, and iterative data optimization based on model eval signals.

You will also drive synthetic data generation and data augmentation to extend coverage into high‑value domains. Your work will directly shape the capability ceiling of in‑house foundation models developed by Bitdeer AI Lab.

• Strong Python engineering skills, with hands‑on experience in Spark, Ray, or similar distributed data processing frameworks for terabyte‑to‑petabyte‑scale workloads.
• Solid experience with large‑scale data processing pipelines, including deduplication (e.g., Min Hash/LSH), format normalization, sampling, and quality classification using rule‑based filters and learned quality classifiers.
• Experience acquiring training data from open‑source corpora, and familiarity with (or willingness to build) large‑scale web crawling, content extraction, and license/compliance handling.
• Experience designing training data mixtures and running data ablations to identify optimal mixing ratios across domains and training stages (pre‑training, mid‑training, and post‑training).
• Experience validating data quality through small‑scale proxy training runs and downstream evaluations, and iterating on data based on eval signals to close the loop between data and model behavior.
• Experience with synthetic data generation and data augmentation, including prompting and distilling from strong LLMs, rejection sampling, and targeted…