Optimize software workloads across modern datacenter and agentic AI applications, identifying performance bottlenecks and developing software, compiler, runtime, and hardware-aware optimizations.
Profile real-world applications and investigate memory, concurrency, and CPU microarchitecture behavior to improve throughput, latency, and efficiency.
Collaborate with compiler, runtime, systems software, hardware, and performance modeling teams to influence future RISC-V CPU architecture.
Analyze and optimize CPU cluster performance including cache hierarchies and interconnects.
Build and use performance models and simulation environments to evaluate architectural concepts.
Lead architectural tradeoff studies across performance, scalability, and power to influence design decisions.
Tenstorrent is leading the industry on cutting-edge AI technology. Their diverse team of technologists is passionate about AI and building the best AI platform, valuing collaboration and curiosity.
Lead the effort to make Runpod the fastest and most cost-efficient place for LLM inference, owning performance end to end.
Profile and diagnose performance bottlenecks across the serving stack, from scheduling to kernels, and implement fixes.
Work closely with product and infrastructure teams to shape how inference is offered, turning improvements into production-ready defaults.
Runpod is the AI Developer Cloud, providing a platform for over one million developers to experiment, train, fine-tune, deploy, and scale AI. We're a small, remote-first team that takes ownership seriously, moves fast, and has processed more than 20 billion inference requests.
Design, develop, and own Nuvacore's cycle-accurate CPU performance simulator as the authoritative reference model for the design team.
Lead microarchitectural exploration across all major CPU domains, from frontend to memory subsystem.
Drive model-to-RTL correlation and mentor engineers across the performance team.
Nuvacore is building ground-up CPU silicon for next-generation compute workloads. Their engineering team is focused on architecture, performance modeling, and cross-team collaboration, with a culture that values technical depth and innovation.
Own performance of Falcon JIT compiler for workloads, from analysis to implementation.
Design and implement optimizations in the middle end and back end, from inlining to vectorization.
Work in a distributed team of compiler experts and contribute to upstream LLVM.
Azul develops Java runtime platforms, including Azul Platform Prime with the Falcon JIT compiler. They operate as a distributed team of compiler experts focused on performance and production reliability.
Build and optimize RL infrastructure for large-scale training.
Improve efficiency across compute, memory, networking, and scheduling.
Design low-level optimizations, from kernels to runtime.
Prime Intellect builds the open superintelligence stack, providing full-stack infrastructure for frontier-scale AI post-training and deployment. The company has raised $150M from top investors and is mission-driven, focused on open-source AI and accelerating science.
Drive adoption of Tenstorrent's AI products by collaborating with enterprise customers and the sales team.
Provide technical expertise in AI/ML to translate complex concepts for non-technical stakeholders.
Partner with sales operations and product teams to shape future product enhancements from customer feedback.
Tenstorrent is an AI technology company developing high-performance RISC-V CPUs and AI platforms. The diverse team of technologists values collaboration, curiosity, and solving hard problems.
Own optimization of the inference stack: throughput, latency, cold starts, and cost per token.
Tune SGLang/vLLM engines, cut TTFT, boost KV cache reuse, and kill cold starts on 1.5 TB checkpoints.
Set technical direction with data, benchmark every change, and scale inference across nodes.
Kimchi is an LLM inference platform serving open models like DeepSeek and GLM from its own GPU fleet via one API line. It's made by CAST AI, a globally distributed team across 34 countries that hit unicorn status in 2026.
Bring new open-source models into production with a focus on latency and throughput.
Optimize inference systems including batching, KV cache, speculative decoding, and quantization.
Design, deploy, and operate heterogeneous clusters across infrastructure vendors to improve reliability and cost efficiency.
They are an early-stage AI infrastructure startup making large language model inference faster and more cost-efficient. The small team focuses on low-level performance engineering and AI-assisted workflows, with a strong emphasis on technical fundamentals.