Researcher developing AI Literacy resources for Digital Promise Global; collaborating with educators and tech partners to foster equitable implementation.
About the role
- AI Trace Generation Engineer designing and implementing trace collection systems for LLM workloads. Analyzing distributed AI workload behavior across multi-GPU and multi-node setups.
Responsibilities
- Design and implement a trace collection system for distributed LLM workloads
- Validate that collected traces accurately reflect real workload behavior
- Integrate with and instrument major LLM frameworks to extract meaningful execution data
- Use collected traces as input to discrete event simulations
- Analyze trace data to surface bottlenecks and inefficiencies across the stack
Requirements
- 3+ years of experience in AI systems, ML infrastructure, or a closely related area
- Hands-on experience with at least one major LLM serving or training framework
- Strong proficiency in Python and C++
- Solid understanding of GPU architecture, memory bandwidth, and the difference between compute-bound and memory-bound operations
- Solid understanding of distributed communication
- Familiarity with parallelism strategies and how they shape execution behavior across large clusters
- Open source contributions or published research in relevant areas will definitely be appreciated
- Previous startup experience is a plus
Benefits
- Competitive compensation with a performance-based incentive
- Subsidized Deutschlandticket
- Access to a discount portal
- Flexible hours with hybrid and remote-friendly options
- Relocation support
Job title
Job type
Experience level
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Not specified
Degree requirement
Tech skills
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