Senior LLM Inference Performance Engineer

Bitdeer (NASDAQ: BTDR); Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Welfare benefits

Training and mentoring

Job summary

Bitdeer is seeking a Senior Inference Runtime Engineer in Singapore to own the performance-critical serving layer of our MaaS platform. You will optimize runtime stacks behind OpenAI- and Anthropic-compatible APIs to make self-hosted LLMs faster, cheaper, and more stable.

Responsibilities include tuning runtimes, profiling bottlenecks, onboarding models, and defining safe defaults for high-value inference workloads. Join a fast-growing team shaping AI-powered mining and cloud services.

Qualifications

• 6+ years of systems, ML infrastructure, or high-performance backend engineering experience.
• Hands-on experience with LLM serving runtimes such as vLLM, Dynamo, SGLang, TensorRT-LLM, TGI, or Triton.
• Strong understanding of GPU memory, CUDA/NCCL basics, KV cache, batching, streaming, and distributed inference tradeoffs.

Responsibilities

• Optimize prefill/decode scheduling, continuous batching, KV cache behavior, speculative decoding, long-context serving, and streaming smoothness.
• Tune and operate vLLM/Dynamo/SGLang/TensorRT-LLM-style runtimes for model-specific latency, throughput, GPU utilization, and cost efficiency.
• Profile bottlenecks across GPU memory, HBM bandwidth, NCCL/network, tokenizer, frontend/proxy, and model worker paths.
• Lead high-value model onboarding, including runtime selection, tensor/pipeline parallelism, quantization, context length, and rollback strategy.
• Define runtime playbooks and safe defaults for reasoning, tool calling, multimodal, prompt cache, and provider-specific parameters.
• Partner with SRE and performance/evaluation engineers to turn benchmark findings into production runtime improvements.

Skills

LLM serving runtimes

Go

Python

GPU memory

CUDA/NCCL basics

Batching/Streaming

Job description

Bitdeer is seeking a Senior Inference Runtime Engineer in Singapore to own the performance-critical serving layer of our MaaS platform. You will optimize runtime stacks behind OpenAI- and Anthropic-compatible APIs to make self-hosted LLMs faster, cheaper, and more stable.

Responsibilities include tuning runtimes, profiling bottlenecks, onboarding models, and defining safe defaults for high-value inference workloads. Join a fast-growing team shaping AI-powered mining and cloud services.