Own and improve the AI inference serving stack, including prefill/decode disaggregation, continuous batching, KV-cache management, and speculative decoding.
Drive GPU performance engineering by identifying and eliminating bottlenecks using CUDA, Triton, profiling, and kernel optimization.
Set the serving and measurement roadmap, establish engineering standards, and mentor engineers while remaining hands-on with implementation.
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.
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.
Build inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters.
Optimize rollout throughput, multi-node MoE inference, and weight-update overhead for long-horizon agents.
Collaborate with model researchers on architecture choices balancing model quality and execution cost.
Prime Intellect is building the open superintelligence stack, unifying compute, environments, and deployment for frontier-scale AI post-training. Backed by $150M in funding, the team is mission-driven and hard-working, focused on accelerating open AI research.
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.
Own the inference stack end-to-end, including pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling.
Invent and validate novel algorithms for fast inference on consumer hardware over the public internet, then put them into production.
Serve both RL training rollouts and final model serving in a permissionless, trustless setting.
Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. They are a world-class, deeply technical team backed by Union Square Ventures and other tier-1 investors.
Own end-to-end performance optimization for production ML workloads, from bottleneck identification to rollout.
Diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, and CPU/GPU utilization.
Design and implement improvements in inference execution, runtime behavior, batching, memory management, and precision.
Reddit is a community of communities, hosting open and authentic conversations across the internet. With over 100,000 active communities and approximately 130 million daily active users, it is one of the web's largest sources of information.
Owns the architectural direction for Code Metal's AI platform and the team building it.
Builds production-grade inference serving, model gateways, agent harnesses, and context-engineering services.
Instruments the stack end to end and drives experimentation and observability for reliable AI systems.
Code Metal provides automated software engineering using AI and formal verification to produce provably correct code. Founded in 2023 and backed by Accel, Salesforce Ventures, and others, it serves defense and enterprise customers from offices in Boston and San Francisco.
Design, build, and evolve systems for distributed model training and batch inference at scale.
Develop workflows for experiment tracking, model evaluation, and registry management.
Optimize training and inference performance across GPU utilization, data loading, and distributed communication.
NOETIK is a frontier therapeutics company using AI to understand human biology and improve oncology outcomes, building foundation models on proprietary multimodal data. It fosters a collaborative, interdisciplinary culture focused on solving hard problems together.
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.
Own end-to-end ML systems from data and training to evaluation, inference, and deployment.
Build and evolve training, fine-tuning, and inference infrastructure for large models.
Partner with research and application engineering to turn model capabilities into product improvements.
Next Step Systems is an IT recruiting and staffing agency that connects companies with machine learning and technology talent. They specialize in placing candidates in remote and on-site roles across the United States and emphasize a fast-paced, results-oriented culture.
Lead customer transitions from proof of concept to production, owning the customer journey and ensuring AI workloads run reliably and scale.
Optimize AI workload performance and reliability by analyzing architectures, improving inference efficiency, and troubleshooting complex issues.
Serve as a trusted technical partner, coordinating production incidents and driving continuous improvements across customer deployments.
Token Factory is an AI infrastructure company that helps businesses bring advanced AI applications from experimentation to reliable production at scale. Its culture is innovative, collaborative, and fast-moving, with a focus on autonomy and meaningful technical impact.
Lead end-to-end technical deployments for GPU neocloud and AI Factory customers.
Configure and troubleshoot bare metal GPU infrastructure including CNI, GPU Operator, and storage.
Build playbooks and transfer knowledge to customer teams for self-sufficiency.
vCluster Labs provides a platform for AI infrastructure, helping GPU cloud providers and enterprises build Kubernetes-based clusters. They are a venture-backed startup with 40+ engineers and a remote-first culture, powering over 100,000 GPUs.
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.
Tenstorrent is leading the industry on cutting-edge AI technology, developing high performance RISC-V CPUs from scratch. Our diverse team of technologists values collaboration, curiosity, and a commitment to solving hard problems.