Bitdeer is hiring a Senior LLM Inference Performance & Evaluation Engineer in AI Cloud — Singapore, SG. The overview below is synthesized from the employer posting on bitdeer.breezy.hr: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
About Bitdeer
Company context from the listing:
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.
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.
What you will be responsible for
- 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.
How you will stand out:
- 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.
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.
Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, colour, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.
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Build a standout application
For the Senior LLM Inference Performance & Evaluation Engineer at Bitdeer, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it.