Research Engineer-Model Efficiency

Bitdeer Technologies Group; Singapore; On-site; SGD 120,000 - 200,000; Full time

Job summary

Bitdeer Technologies Group, via its Bitdeer AI Lab, seeks a skilled professional to optimize LLM inference and ML systems for scalable, cost-efficient deployment. You will work on model optimization—quantization, sparsity, pruning—and matching hardware capabilities to achieve high throughput without quality loss.

You will collaborate across teams to push efficient AI infrastructure, with a focus on transformer internals, rigorous evaluation, and production-ready solutions.

Qualifications

• Bachelor's/Master's/PhD in Computer Science, Electrical Engineering, or a related field, with hands-on experience in LLM inference, model optimization, or ML systems.
• Strong programming ability in Python and deep familiarity with PyTorch; experience with C++, CUDA, or Triton is a plus.
• Genuine implementation-level depth in at least one area of model efficiency, such as quantization, sparsity and pruning, speculative decoding and MTP, or serving-time attention and KV-cache methods.
• Strong understanding of transformer internals and where accuracy loss actually shows up in model behaviour.
• Rigorous evaluation practice — task-level metrics, controlled comparisons, honest baselines.
• Experience taking efficiency methods into production serving, or equivalent research depth, is highly preferred.
• Familiarity with inference engines such as vLLM, SGLang, or TensorRT-LLM and their efficiency features is highly preferred.
• Publications at top-tier systems or ML venues, or substantial open-source contributions, are welcome.

Responsibilities

• Make models cheaper and faster to serve without sacrificing important quality. Implement and adapt published methods on our models and hardware, and develop optimizations where they fall short.
• Build the evaluation discipline to defend claims like 'lossless at 2× throughput.'

Skills

Python

PyTorch

LLM inference

Model optimization

C++

Education

Bachelor's/Master's/PhD in CS/EE

Tools

vLLM

SGLang

TensorRT-LLM

CUDA

Triton

Job description

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.

About Bitdeer AI Lab:

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.

Our mission is to turn energy into intelligence that people can actually afford to use. Inference is where that happens: every product built on a model is bounded by what it costs to run, so the economics of serving decide what gets built at all. We work on this from the ground up, from the power and datacenters we own to the software that turns them into tokens - and we continue to invest in and expand the infrastructure behind it.

What you will be responsible for:

• This role makes models cheaper and faster to serve without giving up quality that matters. We are not prescribing the technique - quantization, sparsity and pruning, speculative decoding and MTP, and serving-time attention and KV-cache methods are all in scope. You will implement and adapt published methods on our models and hardware, and develop your own optimizations where they fall short. You will also build the evaluation discipline that makes a claim like "lossless at 2× throughput" defensible.

How you will stand out:

• Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related field, with hands-on experience in LLM inference, model optimization, or ML systems
• Strong programming ability in Python and deep familiarity with PyTorch; experience with C++, CUDA, or Triton is a plus
• Genuine implementation-level depth in at least one area of model efficiency, such as quantization, sparsity and pruning, speculative decoding and MTP, or serving-time attention and KV-cache methods
• Strong understanding of transformer internals and where accuracy loss actually shows up in model behaviour
• Rigorous evaluation practice — task-level metrics, controlled comparisons, honest baselines — and the ability to state honestly what a number does and does not prove
• Experience taking efficiency methods into production serving, or equivalent research depth, is highly preferred
• Familiarity with inference engines such as vLLM, SGLang, or TensorRT-LLM and their efficiency features is highly preferred
• Publications at top-tier systems or ML venues, or substantial open-source contributions, are welcome
• Deep enthusiasm for cutting-edge AI infrastructure and efficient inference, with a strong ownership mentality and solid engineering discipline

What you will experience working with us:

• A culture that values authenticity and diversity of thoughts and backgrounds;
• An inclusive and respectable environment with open workspaces and exciting start-up spirit;
• Fast-growing company with the chance to network with industrial pioneers and enthusiasts;
• Ability to contribute directly and make an impact on the future of the digital asset industry;
• Involvement in new projects, developing processes/systems;
• Personal accountability, autonomy, fast growth, and learning opportunities;
• Attractive welfare benefits and developmental opportunities such as training and mentoring.