Research Kernel Engineer

Posted 11 Days Ago
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Singapore, SGP
In-Office
Mid level
Software
The Role
Write and optimize CUDA/Triton kernels to implement novel ML efficiency methods; perform performance attribution and roofline analysis across the serving stack; make slow reference implementations production-viable; collaborate with AI Cloud platform team and build measurement foundations for research-driven optimizations.
Summary Generated by Built In

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 exists to make our own research fast. Our efficiency work produces methods — new quantization schemes, sparsity patterns, attention variants, speculative decoding strategies — that have no efficient implementation available anywhere, because they did not exist before. You will write the kernels that turn those methods into real speedups on real hardware, and find the performance headroom that generic open-source implementations leave on the table for our specific models and traffic patterns.

  • Concretely: writing and optimizing CUDA / Triton kernels for methods the team develops; performance attribution across our serving path, including roofline analysis and identifying which kernels dominate under our traffic mix; taking published methods whose reference implementations are too slow to be useful and making them production-viable; and building the measurement basis the rest of the team relies on.

  • Your work is driven by the Lab's research agenda, in close collaboration with the AI Cloud platform team who own the serving stack itself.

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 GPU programming, high-performance computing, or ML systems
  • Proficiency in CUDA and/or Triton, with concrete examples of kernels you have written or meaningfully optimized; strong Python and C++
  • Working knowledge of GPU architecture and memory hierarchy — occupancy, memory coalescing, tensor cores, warp-level primitives — with the profiling habits to back it up (Nsight Compute / Systems or equivalent)
  • Hands-on experience optimizing inference-critical paths such as attention, GEMM, normalization, sampling, or KV-cache management
  • Comfortable implementing methods that have no reference implementation available — working from a paper, a colleague's notebook, or a whiteboard sketch
  • Ability to define your own measurement before optimizing, and to state honestly what a number does and does not prove
  • Experience with inference engines such as vLLM, SGLang, or TensorRT-LLM, including writing custom kernels or extensions for them, is highly preferred
  • Compiler or IR-level experience (MLIR, TVM, TorchInductor), or experience with distributed serving (tensor / pipeline parallelism), is highly preferred
  • Publications at top-tier systems venues, or substantial open-source contributions to inference or GPU computing projects are welcome
  • Deep enthusiasm for cutting-edge AI infrastructure and squeezing real performance out of hardware, 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.

Skills Required

  • Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or related field with hands-on GPU programming, HPC, or ML systems experience
  • Proficiency in CUDA and/or Triton with concrete examples of kernels written or optimized
  • Strong Python and C++ skills
  • Working knowledge of GPU architecture and memory hierarchy, and profiling tools (Nsight Compute / Nsight Systems or equivalent)
  • Hands-on experience optimizing inference-critical paths (attention, GEMM, normalization, sampling, KV-cache management)
  • Ability to implement methods without reference implementations (from papers/notebooks/whiteboard)
  • Ability to define and run measurements, and interpret performance data honestly
  • Experience with inference engines (vLLM, SGLang, TensorRT-LLM) and writing custom kernels/extensions
  • Compiler or IR-level experience (MLIR, TVM, TorchInductor) or distributed serving (tensor/pipeline parallelism)
  • Publications at systems venues or substantial open-source contributions to GPU/inference projects
  • Strong ownership mentality and engineering discipline
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The Company
HQ: Singapore
214 Employees

What We Do

Bitdeer Technologies Group (Nasdaq: BTDR) is a leader in the blockchain and high-performance computing industry. It is one of the world’s largest holders of proprietary hash rate and suppliers of hash rate. Bitdeer is committed to providing comprehensive computing solutions for its customers. The company was founded by Jihan Wu, an early advocate and pioneer in cryptocurrency who cofounded multiple leading companies serving the blockchain economy. Mr. Wu leads the company as Founder, Chairman, and CEO. Linghui Kong serves as Bitdeer’s CBO and provides leadership through deep industry knowledge and technology expertise. Headquartered in Singapore, Bitdeer has deployed mining datacenters in the United States, Norway, and Bhutan. It offers specialized mining infrastructure, high-quality hash rate sharing products, and reliable hosting services to global users. The company also offers advanced cloud capabilities for customers with high demands for artificial intelligence. Dedication, authenticity, and trustworthiness are foundational to our mission of becoming the world’s most reliable provider of full-spectrum blockchain and high-performance computing solutions. We welcome global talent to join us in shaping the future

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