Senior LLM Inference Performance & Evaluation Engineer

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

• Build benchmark pipelines for TTFT, inter-token latency, output throughput, request latency, concurrency curves, error rate, and GPU utilization.
• Create model launch gates covering OpenAI/Anthropic compatibility, streaming behavior, tool calling, reasoning output, multimodal behavior, and long-context cases.
• Maintain representative workloads using synthetic, replayed, and customer-like traffic patterns for both steady-state and burst scenarios.
• Compare model/runtime/provider options and publish clear recommendations for routing, fallback, pricing, and capacity decisions.
• Automate regression detection in CI/CD and staging so model, runtime, or configuration changes do not silently degrade quality, latency, or cost.
• Work with runtime engineers to identify bottlenecks and verify improvements; work with SRE to convert benchmark results into SLOs and alert thresholds.

• 5+ years in ML infrastructure, performance engineering, model evaluation, QA automation, or backend testing for production systems.
• Experience with LLM serving metrics such as TTFT, TPOT/ITL, request latency, token throughput, concurrency, and GPU utilization.
• Strong Python skills for benchmark/evaluation automation; Go experience is a plus for integrating with platform services.
• Familiarity with OpenAI/Anthropic APIs, vLLM/Dynamo/SGLang/Triton-style servers, and Kubernetes-based test environments.
• Able to design statistically meaningful tests and communicate tradeoffs between model quality, latency, reliability, cost, and customer experience.
• Experience building dashboards, reports, and release gates for engineering, product, and business stakeholders.

• 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.

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