Applied Scientist, Agent Evaluation & Adaptive Model Routing

About Bitdeer

Bitdeer is a world-leading technology company for Bitcoin mining and AI cloud. It provides comprehensive Bitcoin mining solutions, designs industry-leading ASIC chips, manufactures mining rigs, and manages complex processes across the value chain, including equipment procurement, transport logistics, datacenter design and construction, equipment management, and network and facility operations. Bitdeer also offers advanced cloud capabilities for customers with 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, it is committed to exploring the frontiers of artificial intelligence with the ambition to build technologies that can change the world. Its mission is to turn energy into intelligence that people can afford to use. Inference is central to this mission, as the economics of serving determine what gets built. The lab works from the ground up, leveraging Bitdeer’s owned power and datacenters and the software that turns them into tokens, while continuing to invest in and expand the underlying infrastructure.

What You Will Be Responsible For

• Build and extend the LLM and agent evaluation pipeline, including developing representative task suites and measuring behavior at the trajectory level across task success, tool use, quality, cost, latency, and token consumption.
• Research and prototype adaptive model-routing strategies within a shared agent harness, including rule-based baselines, cascades, stage-aware routing, uncertainty-aware selection, and escalation and recovery policies.
• Take promising methods from offline evaluation through traffic replay, shadow testing, and internal pilots, while collaborating with the MaaS and platform engineering teams on production integration.
• Own evaluation methodology, routing policy, and research prototypes; collaborate on production gateway reliability, billing, access control, and SLA engineering with MaaS and platform teams.

How You Will Stand Out

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Statistics, Electrical Engineering, or a related field, with substantial hands-on experience in LLM evaluation, agentic systems, applied machine learning, or adaptive inference.
• Strong programming ability in Python and practical experience with PyTorch and modern data and evaluation tooling; able to independently build reliable experimental pipelines and internal research prototypes.
• Demonstrated experience designing or operating evaluation pipelines for LLMs or agents, including task-level and trajectory-level metrics, dataset construction, automated scoring, regression testing, and failure analysis.
• Implementation-level depth in at least one core area: model selection and routing, uncertainty estimation and calibration, cascading and escalation, or stage-aware agent inference. Experience with preference modeling, contextual bandits, online learning, or broader adaptive inference methods is a plus.
• Hands-on experience evaluating multi-turn or tool-using agents, including task completion, tool-call correctness, planning failures, recovery behavior, and cost and latency trade-offs.
• Rigorous experimental practice—controlled comparisons, honest baselines, statistical analysis, and the ability to explain clearly what an evaluation result does and does not prove.
• Ability to analyze per-request and per-trajectory quality, cost, latency, and token usage, and construct cost-quality Pareto frontiers rather than relying only on aggregate model scores.
• Experience with multi-model APIs, agent harnesses, traffic replay, shadow evaluation, A/B testing, or production model monitoring is highly preferred.
• Familiarity with model-specific differences in tool calling, context windows, prompt caching, reasoning controls, and inference systems such as vLLM or SGLang is a plus.
• Publications at top-tier ML, NLP, or systems venues, or substantial open-source contributions in evaluation, agents, routing, or inference, are welcome.
• Strong ownership and product judgment, with a track record of taking ambiguous research questions from problem definition through a working 0-to-1 prototype and measurable internal validation.

What You Will Experience Working With Us

• A culture that values authenticity and diversity of thoughts and backgrounds.
• An inclusive and respectful environment with open workspaces and an exciting start-up spirit.
• A 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 and the development of processes/systems.
• Personal accountability, autonomy, fast growth, and learning opportunities.
• Attractive welfare benefits and developmental opportunities such as training and mentoring.